Agentic AI Engineer
About the Opportunity
We're partnering with a globally recognized enterprise organization that is
making a significant investment in next-generation AI capabilities and building a world-class AI Engineering function.
This team is focused on developing intelligent systems that combine
cutting-edge AI research with large-scale enterprise deployment, tackling some of the most challenging problems in reasoning, orchestration, retrieval, memory management, and autonomous workflow execution.
As an Agentic AI Engineer, you'll play a key role in designing and building production-grade AI systems capable of reasoning, planning, retrieving information, using tools, and executing complex workflows at scale. You'll work alongside AI researchers, platform engineers, architects, and product leaders to help define how intelligent systems operate within real-world enterprise environments.
This is not a prompt-engineering-only role. We are looking for engineers who think deeply about system behavior, context management, grounding, reliability, and how intelligent agents perform under real-world operational constraints.
Work You'll Do
As an Agentic AI Engineer, you will design, build, and operationalize LLM-powered systems capable of reasoning, planning, retrieving information, using tools, and executing multi-step workflows reliably at scale.
You will work on the "thinking layer" of modern AI systems, including:
• Agent architecture and orchestration
• Tool integration and workflow execution
• Retrieval and grounding pipelines
• Memory and context management
• Evaluation and observability
• Reliability, safety, and guardrails
You will help shape how complex domain knowledge is transformed into production-grade AI behavior, with a strong emphasis on precision, traceability, maintainability, and operational robustness.
Key Responsibilities
• Design and implement agentic AI systems capable of multi-step reasoning, planning, tool use, and workflow execution.
• Build stateful workflows using frameworks such as LangGraph and LangChain, including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns.
• Develop and integrate Retrieval-Augmented Generation (RAG) pipelines, including ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
• Engineer memory and context management capabilities, including conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context engineering.
• Build integrations with internal and external tools, APIs, enterprise systems, databases, and model providers so agents can operate safely within real business workflows.
• Contribute to context delivery and model interaction patterns that improve how AI systems discover, retrieve, and use relevant information.
• Evaluate system quality across both retrieval and generation layers using automated metrics, human review, and task-based evaluation frameworks.
• Implement observability for prompts, tool calls, retrieval quality, agent traces, failures, drift, latency, and production behaviour.
• Apply guardrails, safety controls, and failure handling mechanisms to improve reliability and reduce hallucinations or unsafe actions.
• Stay current on advances in LLMs, agentic systems, evaluation methodologies, and context engineering, translating research and emerging techniques into practical engineering decisions.
Required Qualifications
• Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
• Hands-on experience building production-grade applications with LLMs, including prompt engineering, tool use, structured outputs, error handling, and model behaviour tuning.
• Strong experience with LangChain and especially LangGraph for orchestrating complex LLM workflows and agent behaviour.
• Experience designing and optimizing end-to-end RAG systems, including indexing, retrieval, reranking, grounding, and evaluation.
• Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal context selection.
• Deep understanding of LLM behaviour in practice, including strengths, limitations, hallucination risks, reasoning constraints, latency/cost trade-offs, and evaluation methods.
• Strong Python engineering skills and familiarity with modern software engineering practices, including testing, CI/CD, version control, and API integration.
• Experience implementing observability, tracing, and debugging for LLM-based systems in production.
• Ability to translate ambiguous, high-complexity business processes into robust system logic and reusable AI patterns.
Preferred Qualifications
• Experience with multi-agent systems and agent collaboration patterns.
• Familiarity with vector databases and retrieval infrastructure such as Pinecone, Weaviate, or Milvus.
• Exposure to model adaptation and fine-tuning techniques such as LoRA or QLoRA.
• Understanding of traditional NLP concepts including tokenization, semantic similarity, entity extraction, summarization, and transformer fundamentals.
• Demonstrated habit of staying current with AI research, benchmarks, and emerging engineering patterns.
• Experience operating in highly regulated, high-stakes, or operationally complex enterprise environments.
The Team
You will join a multidisciplinary team of AI engineers, platform architects, researchers, and technical leaders building the next generation of enterprise AI systems.
The team is focused on solving highly complex real-world challenges where reliability, explainability, scalability, and intelligent decision-making are critical. You'll work on systems that move beyond simple chatbot experiences into sophisticated reasoning, planning, retrieval, and workflow automation capabilities.
Success in this role requires a strong engineering mindset, curiosity, and a passion for building the underlying machinery that powers intelligent systems—not just the interfaces users see.
Why Apply?
• Work on some of the most exciting areas in modern AI, including Agentic AI, RAG, memory systems, orchestration, and reasoning
• Help build production-grade AI systems operating at enterprise scale
• Collaborate with highly technical engineers, researchers, and architects
• Access modern AI tooling, infrastructure, and platforms
• Join a rapidly growing AI organization with significant executive sponsorship and long-term investment
• Competitive compensation and strong career growth opportunities
Experimentation Analytics Manager (IC)
Location: NYC or NJ (Hybrid)
Work Model: Hybrid (minimum 3 days onsite)
Salary: $110,000-$130,000 + Bonus
This position is not open for C2C and sponsorship is not available now or in the future.
About the Role
A leading media and entertainment organization is seeking an Experimentation Analytics Lead to drive A/B testing strategy across a portfolio of high-traffic digital brands spanning news, sports, streaming, and entertainment.
This role will partner closely with Product, Analytics, Marketing, Editorial, Growth, and Engineering teams to design and scale experimentation initiatives that improve engagement, conversion, and overall digital performance.
Key Responsibilities
- Own and prioritize the experimentation roadmap across product, growth, and marketing initiatives.
- Design, execute, and analyze A/B and multivariate tests end-to-end.
- Translate business questions into measurable hypotheses and experimentation frameworks.
- Identify trends and actionable insights across multiple concurrent tests.
- Build scalable experimentation best practices and governance processes.
- Present findings and recommendations to technical and business stakeholders.
Qualifications
- 5+ years of experience in experimentation, product analytics, or related quantitative fields.
- Strong understanding of hypothesis testing, experimental design, and causal inference.
- Hands-on experience with experimentation platforms such as Amplitude or similar tools.
- Strong SQL skills plus experience with Python or R.
- Experience with tools such as Snowflake, Databricks, Adobe Analytics, Google Analytics, Tableau, Looker, or Omni.
- Strong communication and cross-functional stakeholder management skills.
This is a highly visible opportunity to help shape experimentation strategy and influence data-driven decision-making across large-scale consumer platforms.
Founding Software Engineer | AI Developer Infrastructure | Rust / TypeScript | NYC
Location: New York City (5 days onsite)
I'm working with an exceptionally well-funded early-stage startup that's building infrastructure for the next generation of AI-powered software development.
Their platform gives engineering leaders visibility into how AI coding agents perform across the software development lifecycle—tracking AI-generated code from prompt through production to help teams measure quality, reliability, and developer productivity.
Already seeing strong enterprise adoption, they're now looking to hire three Founding Engineers to help define both the product and the company.
The Opportunity
This is a genuine founding engineering role where you'll own major technical decisions from day one.
You'll work across architecture, product development, infrastructure, and customer deployments while collaborating directly with the founding team and early enterprise customers.
The engineering challenges are deeply technical, spanning developer tooling, distributed systems, observability, Git internals, and large-scale data processing.
What You'll Be Building
-
Core systems that track AI-generated code throughout the software development lifecycle
-
High-performance pipelines for ingesting, processing, and querying developer and AI agent activity
-
Integrations with leading AI coding assistants and developer platforms
-
Observability systems that measure agent performance, code quality, and engineering productivity
-
Infrastructure supporting secure, high-performance enterprise deployments
-
Developer tooling that operates transparently with virtually no workflow disruption
-
Open standards for AI code attribution and developer telemetry
You'll also work closely with design partners and enterprise customers, helping shape both the technical roadmap and the future direction of the platform.
What We're Looking For
You'll likely bring:
-
5+ years of professional software engineering experience
-
Experience working in high-growth startups (Seed through Series C)
-
Strong backend, infrastructure, distributed systems, or full-stack engineering experience
-
Experience building production systems where performance, scalability, and reliability matter
-
A high level of ownership and the ability to thrive with ambiguity
-
Strong product instincts and an interest in building tools that developers genuinely enjoy using
The engineering team primarily works with Rust and TypeScript, although experience in those languages isn't a prerequisite.
Bonus experience includes:
-
Developer tools or developer infrastructure
-
Git internals
-
Observability or telemetry platforms
-
Distributed systems and high-scale backend infrastructure
Why This Role?
-
Founding engineer opportunity with meaningful equity
-
Solve genuinely difficult engineering problems at the forefront of AI software development
-
Build products used by engineering teams at leading enterprises
-
Work directly alongside experienced, repeat founders
-
Shape engineering culture, product direction, and technical architecture from the earliest stages
-
Join a small, highly technical team where every engineer has significant ownership
Compensation
-
Base Salary: $190,000–$220,000
-
Meaningful founding equity package
-
Medical, dental, and vision coverage
-
In-person collaboration in New York City
-
High-autonomy, fast-moving engineering culture
If you're excited by developer infrastructure, distributed systems, AI tooling, or building category-defining products from the ground up, I'd be happy to share more details confidentially.
Analyst or Sr Analyst - Measurement Data Analytics Opportunity
I’m searching for an Analyst or Sr Analyst, Measurement Data to work for a media and digital organization focused on audience intelligence and cross-platform performance insights.
This team sits within a centralized analytics group responsible for understanding how content, advertising, and audience behavior are measured across linear, streaming, and digital platforms. In this role, you would work hands-on with large-scale datasets, support reporting and metric development, and help interpret changes in third-party measurement data. You’ll collaborate with multiple internal teams and external vendors, ensuring data quality, consistency, and clear insights that drive business decisions. A key part of the role is working with evolving industry datasets and helping translate complex measurement outputs into actionable findings for stakeholders.
Key experience
• SQL skills and comfort working with Big Data
• Experience working with media, analytics, or measurement data (agency, ad sales, Nielsen / comScore type exposure a plus)
• Familiarity with digital or linear audience measurement concepts
• Exposure to reporting tools and structured data environments (Excel, BI tools, or similar)
• Strong attention to detail and ability to track data changes and methodology updates
• Ability to communicate findings clearly across multiple teams and stakeholders
This is a full-time opportunity and unfortunately sponsorship is not available currently (US Citizen or Green Card only).
If interested, please apply here!
LLM Modeling & Post-Training Engineer
About the Opportunity
We're partnering with a globally recognized enterprise organization that is making a significant investment in next-generation AI capabilities and building a world-class AI Engineering function.
This team is focused on developing advanced AI systems that combine frontier AI research with large-scale enterprise deployment, tackling some of the most challenging problems in reasoning, alignment, reliability, safety, and model optimization.
As an LLM Modeling & Post-Training Engineer, you'll play a key role in shaping the behavior, performance, and reliability of large language models used across complex, high-impact environments. Working alongside AI researchers, platform engineers, architects, and product leaders, you'll help bridge the gap between cutting-edge AI research and production-grade systems operating at scale.
This role focuses on the core modeling layer of modern AI systems - supervised fine-tuning, reinforcement learning, preference optimization, reward modeling, alignment, evaluation, and large-scale training workflows.
We are looking for individuals who understand not only how to use LLMs, but how to improve and shape model behavior through advanced post-training techniques.
This is not a prompt-engineering-only role. The ideal candidate has hands-on experience with fine-tuning and aligning open-weight or proprietary foundation models using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, LoRA, and QLoRA, and understands how these methods impact reasoning quality, safety, latency, cost, and production reliability.
Work You'll Do
As an LLM Modeling & Post-Training Engineer, you will design, train, evaluate, and optimize large language models for enterprise use cases involving complex reasoning, domain specialization, structured outputs, workflow execution, and alignment with business objectives.
You will work across the full post-training lifecycle, including:
• Supervised fine-tuning (SFT)
• Reinforcement learning and alignment
• Preference optimization and reward modeling
• Synthetic data generation and curation
• Evaluation and benchmarking
• Model safety and red teaming
• Distributed training and inference optimization
• Productionization of fine-tuned models
You will collaborate with platform engineers, domain experts, AI architects, and product leaders to translate complex business requirements into high-performing, reliable, and controllable AI systems.
The Team
You will join a multidisciplinary team of AI researchers, modeling engineers, platform engineers, architects, and domain specialists building next-generation enterprise AI systems.
The team operates at the intersection of frontier AI research and large-scale enterprise engineering, tackling highly complex reasoning challenges, evolving operational workflows, and demanding reliability requirements.
Success in this role requires strong technical depth, intellectual curiosity, an experimentation mindset, and the ability to translate advanced AI research into practical, production-ready systems.
Why Apply?
• Work on cutting-edge AI challenges involving reasoning, alignment, and post-training
• Help shape the next generation of enterprise AI systems
• Collaborate with highly technical researchers, engineers, and architects
• Access modern AI infrastructure, tooling, and large-scale training environments
• Join a growing AI organization with significant executive sponsorship and long-term investment
• Competitive compensation and strong career growth opportunities
If you have worked on humanoid robots or dexterous hands from any side please apply. You can have a background as an IC, or in Leadership across software, Ai, autonomy, mechanical, and electrical engineering.
I am working with one of the top Humanoid companies.
Unsure of your next step? Reach out.
At Lawrence Harvey, we recognise that navigating the job market can be daunting. Whether you're an experienced professional seeking a fresh challenge or starting your career, our team of dedicated tech recruitment specialists are here to assist you. With our vast network of industry contacts and thorough understanding of the tech landscape, we can offer guidance and support to help you discover the ideal opportunity.