Lead AI Engineer - Generative AI & Agentic AI: Cogentiq

Role Overview

We are looking for an experienced Lead AI Engineer - Generative AI & Agentic AI to design, build and productionize enterprise-scale AI solutions. This is a hands-on engineering role for someone who combines strong software engineering fundamentals with deep expertise in AI/ML, Generative AI, LLMs and modern AI architecture.

The ideal candidate will be comfortable taking AI solutions from experimentation to production, working across models, data, APIs, cloud infrastructure and enterprise applications, while providing technical leadership to engineering teams.

What You'll Do

• Architect, build and productionize AI/ML and Generative AI solutions for complex enterprise use cases.
• Design and develop LLM-powered applications using RAG, AI agents, tool/function calling, orchestration and multi-agent workflows.
• Build scalable AI services and backend applications using Python, REST APIs and modern microservices architectures.
• Design end-to-end RAG architectures incorporating embeddings, vector search, retrieval, reranking and enterprise knowledge sources.
• Develop AI applications using frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex or equivalent.
• Work with leading LLM platforms such as OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini or equivalent.
• Design and implement practices around LLM evaluation, observability, guardrails, security, latency and cost optimization.
• Build robust AI/ML pipelines covering experimentation, deployment, monitoring and continuous improvement.
• Integrate AI solutions with enterprise applications, APIs, databases, data platforms and cloud services.
• Evaluate emerging AI models, frameworks and technologies and translate them into practical enterprise solutions.
• Drive technical design discussions and contribute to architecture decisions across AI platforms and products.
• Establish engineering best practices for building secure, scalable and maintainable AI applications.
• Mentor engineers and provide technical leadership across AI engineering initiatives.
• Partner with product managers, data scientists, architects and business stakeholders to convert business problems into scalable AI solutions.

What We're Looking For

• 10+ years of overall experience in software engineering, AI/ML engineering or related technology roles.
• Strong hands-on development experience in Python, with excellent software engineering and system-design fundamentals.
• Strong understanding of Machine Learning, Generative AI and Large Language Models.
• Demonstrated experience building and deploying GenAI/LLM applications into production.
• Hands-on experience with RAG, embeddings, vector search, prompt engineering, LLM orchestration and AI agents.
• Experience designing agentic AI workflows, including tool/function calling and complex or multi-agent orchestration.
• Experience working with at least one major LLM ecosystem such as OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini or equivalent.
• Strong understanding of REST APIs, microservices, distributed systems, databases and enterprise application architecture.
• Hands-on experience with at least one major cloud platform: AWS, Azure or GCP.
• Experience with LLM evaluation, monitoring, observability, responsible AI and guardrails.
• Ability to independently own the technical architecture and implementation of complex AI solutions.
• Strong problem-solving, communication and technical leadership skills.
• Experience mentoring engineers and influencing engineering/architecture decisions.

Good to Have

• Experience with knowledge graphs, ontologies, semantic layers or GraphRAG.
• Experience with MLOps/LLMOps, model serving, CI/CD and AI observability platforms.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, pgvector, Redis or equivalent.
• Experience with fine-tuning, model adaptation and open-source LLMs.
• Experience with AI security, governance, privacy and enterprise AI architecture.
• Hands-on exposure to PyTorch, Hugging Face, LangGraph, LangChain, Semantic Kernel or similar frameworks.
• Experience leading small engineering teams or mentoring senior engineers.
• Understanding of data engineering, streaming, distributed data processing and modern cloud data platforms.

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science or a related quantitative/technical discipline.
• Strong academic background, preferably from a premier engineering/technology institution with a demonstrated record of academic and technical excellence.

What Will Make You Stand Out

We are particularly interested in engineers who can demonstrate that they have personally architected and built production GenAI systems, rather than simply experimented with LLMs.

Strong candidates will be able to discuss real-world experience around:

• Production RAG and Agentic AI architectures
• Building scalable Python/API-based AI services
• LLM evaluation and observability
• Enterprise data and application integration
• Security and guardrails
• Latency and inference-cost optimization
• Taking an AI solution from prototype → production → scale