Lead AI Engineer

EPAM Systems Inc

  • Atlanta, GA
  • 30+ days ago

    Highlights

    As a technical leader, you wont just write code; you will shape our AI technical roadmap, architect complex enterprise-grade systems, act as a trusted advisor to our clients, and mentor and grow a high-performing engineering team. Drive technical vision and architecture by leading the design and implementation of highly scalable, robust enterprise AI applications, including complex multi-agent workflows, custom search engines, and end-to-end AI systems.

    Numbers & Facts

    LocationAtlanta, GA

    Description

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    Lead AI Engineer

    Remote in Georgia, & 4 others

    AI Engineering

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    We are searching for a skilled, innovation-driven Lead AI Engineer to join our team and spearhead the development of cutting-edge Generative AI solutions. If youre passionate about artificial intelligence and adept at building systems that deliver measurable business impact, this opportunity is ideal for you. You will work with a dynamic group of professionals to create and deploy AI-driven technologies that tackle real-world challenges - all within a highly collaborative, supportive, and forward-focused environment.

    As a technical leader, you wont just write code; you will shape our AI technical roadmap, architect complex enterprise-grade systems, act as a trusted advisor to our clients, and mentor and grow a high-performing engineering team.

    If you love Python, Natural Language Processing (NLP), and driving innovation at the intersection of AI strategy and software craftsmanship, we want to hear from you!

    Responsibilities

    • Drive technical vision and architecture by leading the design and implementation of highly scalable, robust enterprise AI applications, including complex multi-agent workflows, custom search engines, and end-to-end AI systems

    • Partner closely with clients and business stakeholders to translate business goals into technical roadmaps and recommend high-impact, feasible LLM-driven solutions

    • Establish engineering standards by setting the benchmark for code quality, architectural patterns, robust data pipelines, smart prompt management, and automated evaluation frameworks

    • Foster a culture of learning and high performance through design reviews, development best practices, and active mentorship of junior and senior developers

    • Direct R&D and innovation by guiding research on emerging models, framing prototyping strategies (PoCs), and standardizing the tools and frameworks used to stay on the bleeding edge of Generative AI

    Requirements

    • 5+ years of experience in a Lead, Principal, or Senior AI/Software Engineering role, with a track record of architecting and shipping complex, production-grade AI or ML-based solutions

    • At least 1 year of relevant leadership experience

    • Master-level proficiency in Python (specifically FastAPI or equivalent web frameworks) and a strong foundation in core NLP concepts (semantic similarity, tokenization, NER, text classification)

    • Expertise in modern Generative AI design patterns, particularly RAG (Retrieval-Augmented Generation), autonomous multi-agent networks, and tool integration

    • Production experience with major LLM APIs (OpenAI, Anthropic, Bedrock, Gemini) and framework orchestration tools such as LangGraph or LlamaIndex

    • Background in optimizing AI applications for performance, security (prompt injection mitigation, data privacy), cloud costs, and system evaluation using programmatic or LLM-as-a-judge approaches

    • Capability to quickly direct and build intuitive UI/UX prototypes using Streamlit, Gradio, or equivalent

    • Outstanding leadership and communication skills to lead engineering discussions, bridge the gap between technical and non-technical audiences, and present technical architectures to executives and clients

    • Strong English communication skills (B2 level or higher)

    Nice to have

    • Full-stack capabilities with TypeScript

    • Experience incorporating AI into the SDLC to boost team efficiency using tools like GitHub Copilot, Cursor, or specialized agents for automation

    • Cloud architecture expertise in AWS or Azure (specifically Azure OpenAI, AWS Bedrock, or cloud-native containerized deployments)

    • Familiarity with Databricks (MLflow, Agent Bricks, Unity Catalog) for orchestrating data pipelines and model lifecycles

    • Knowledge of advanced search/retrieval systems (hybrid search, vector databases, custom rerankers and ranking algorithms)

    • Familiarity with emerging open-source protocols (like Model Context Protocol - MCP) and LLM monitoring tools (like LangSmith or Arize Phoenix)

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