Lead AI Engineer

We are seeking a highly capable Lead AI Engineer to design, build, and scale enterprise-grade agentic AI systems that operate reliably in production environments.

This role requires strong hands-on expertise in agent orchestration, distributed systems, LLM application engineering, retrieval architectures, and production AI delivery. The ideal candidate is not only technically strong, but also able to lead implementation decisions, guide engineers, and translate evolving business problems into robust AI systems.

You will work closely with product teams, data scientists, platform engineers, and business stakeholders to architect intelligent agent workflows, memory-aware reasoning systems, tool-driven execution pipelines, and cloud-ready AI platforms.

Experience: 8+ years (Full-time)
Location- Remote Key Responsibilities

Technical Leadership

• Lead design and implementation of scalable agentic AI systems for enterprise use cases

• Own architecture decisions across orchestration, memory, tool execution, retrieval, and system reliability

• Translate ambiguous business requirements into modular technical solutions with clear execution plans

• Guide engineering teams on design patterns, implementation standards, and production readiness

• Drive technical reviews, design discussions, and solution trade-off decisions

Agentic AI Engineering

• Build multi-agent systems involving planning, reasoning, execution, coordination, and controlled autonomy

• Implement agent workflows with state handling, retries, guardrails, memory persistence, and fault recovery

• Design agent communication patterns including sequential, hierarchical, and collaborative orchestration

• Build tool-first execution models integrating APIs, databases, enterprise systems, and external services

• Implement short-term and long-term memory patterns across agent sessions

LLM Systems & Prompt Engineering

• Design production-grade LLM pipelines including prompt orchestration, function calling, structured outputs, and tool invocation

• Apply advanced prompt engineering techniques including:

  • Zero-shot and few-shot prompting

  • Chain-of-thought reasoning

  • Reflection and iterative prompting

  • Prompt optimization for reliability and cost control

• Build robust guardrails for hallucination reduction, response validation, and deterministic behavior

Production Engineering

• Build fault-tolerant AI systems with clear success/failure handling

• Ensure observability through logging, tracing, and execution monitoring

• Implement evaluation pipelines for prompts, agents, and retrieval quality

• Drive performance tuning for latency, cost, and scalability

• Ensure enterprise compliance including security, access control, and data governance

Team Collaboration & Delivery

• Partner with Data Scientists to integrate ML models into agent workflows

• Work with platform teams to productionize AI systems across cloud environments

• Contribute actively within Agile delivery cycles

• Mentor engineers and raise technical standards across the team

Required Experience & Expertise

Professional Experience

• 8+ years of industry experience in AI/ML and Intelligent systems development

• Proven experience delivering AI or ML solutions in large-scale or enterprise environments

• Strong understanding of Agentic AI architectures, including both neural-based and symbolic agents

• Hands-on experience building multi-agent systems, including:

  • Agent collaboration and coordination

  • Reinforcement learning or feedback-driven optimization

  • Dynamic or flexible workflows

  • State, caching, and memory management

• Experience with one or more agentic AI frameworks, such as:

  • LangGraph / LangChain

  • CrewAI

  • Semantic Kernel

  • AutoGen or equivalent frameworks

Programming & ML

• Strong proficiency in Python for building scalable, production-grade systems

• Experienced or foundational knowledge in machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or AutoML tools

• Solid understanding of model lifecycle management, including training, evaluation, and deployment

Prompt Engineering & LLMs

• Practical experience with prompt engineering techniques, including:

  • Zero-shot and few-shot prompting

  • Chain-of-thought and structured reasoning

  • Prompt iteration and optimization

• Experience building LLM-based applications, including tool use and function calling

IR / RAG & Knowledge Systems

• Experience designing and implementing Information Retrieval (IR) and RAG systems

• Hands-on work with vector databases, embeddings, and optionally knowledge graphs

• Familiarity with hybrid search approaches (vector + lexical + metadata-based retrieval)

Model Evaluation

• Experience evaluating AI systems using quantitative and qualitative metrics

• Familiarity with A/B testing, benchmarking, and performance analysis of LLMs and prompts

Technical Skills

• Programming Languages: Python (required)

• Agentic AI: LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, OpenAI Agent SDK, or similar

• Generative AI: LLMs, RAG architectures, NLP pipelines

• Cloud Platforms: Experience with at least one major cloud provider (e.g., Google Cloud Platform, Azure, AWS); ability to design cloud-agnostic architectures

• Version Control: Git / GitHub

• Development Practices: Model testing, validation, CI/CD awareness

• Collaboration: Experience working in Agile / Scrum teams

What Success Looks Like

• The candidate designs and mentors the team to implement enterprise-grade agentic AI solutions with minimal supervision and zero hand-holding.

• Translates ambiguous or loosely defined business requirements into well-architected, scalable, and production-ready agentic systems.

• Delivers solutions that adhere to enterprise IT, security, and compliance standards, including data governance and access controls.

• Builds fault-tolerant, resilient agentic software, with clear handling of both success and failure scenarios.

• Implements comprehensive testing strategies, covering positive paths, edge cases, and failure modes, incorporating explicit business validation inputs.

• Proactively collaborates with cross-functional team members to promote shared learning, technical excellence, and best practices.

• Acts as a reliable team contributor during high-pressure situations, including production incidents or critical system failures, supporting root-cause analysis and rapid recovery.

• Demonstrates ownership, accountability, and a production-first mindset throughout the lifecycle of agentic AI solutions.