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Sr. AI Engineer/Gen AI

Summary

Builds and deploys enterprise-grade generative AI systems, including LLM pipelines, RAG, and agentic workflows, using Python, React, and frameworks like LangChain.

Key Responsibilities

Generative AI Engineering & Architecture

  • Lead the architecture, development, and production deployment of enterprise-grade Generative AI systems with a focus on scalability, reliability, and performance.
  • Design and implement agentic workflows using LLMs, tools, and orchestration frameworks to enable intelligent autonomous behaviors.
  • Build and integrate end-to-end GenAI pipelines, including model inference, RAG (Retrieval-Augmented Generation), embeddings, and application consumption via APIs.
  • Collaborate with data and platform engineering teams to implement vector search, semantic retrieval, and contextual grounding for AI driven responses.
  • Apply strong full stack engineering skills to build user-facing GenAI applications and robust backend systems.
  • Develop scalable microservices and secure RESTful APIs to expose AI and ML capabilities across enterprise applications.
  • Integrate GenAI functions with front-end interfaces built using modern web technologies.

Deployment, Operations, & Quality

  • Lead model deployment, packaging, performance optimization, and lifecycle management in production environments.
  • Ensure reliability and quality through test driven development (TDD), reusable component design, and modern architectural principles.
  • Drive CI/CD based releases, containerized deployments, and DevOps practices for AI-driven applications.
  • Provide technical mentorship to intermediate engineers and contribute to architectural design reviews.
  • Champion engineering best practices, coding standards, and continuous improvement within Agile delivery teams.

Required Skills & Technical Expertise

Programming & Full Stack Development

  • Proficiency in Python, Java, R, and SQL.
  • Frontend and full stack development experience with ReactJS, HTML, CSS, and Node.js.

AI / ML & Generative AI

  • Hands-on experience with TensorFlow, Keras, and PyTorch.
  • Strong foundations in Generative AI, NLP, and NLU.
  • Expertise working with Large Language Models (LLMs) and prompt engineering.

Agentic & GenAI Frameworks

  • Experience with LangChain and familiarity with LangGraph and Ollama.
  • Understanding of agentic system design, tool use, and workflow orchestration patterns.

Retrieval & Knowledge Foundations

  • Strong knowledge of vector databases, RAG pipelines, semantic search, and embedding techniques.
  • Demonstrated experience in microservices architecture, service-oriented design, and building scalable REST APIs.
  • Expertise in API-driven development and integration patterns.
  • Experience with model packaging, containerization, deployment pipelines, and operational monitoring.
  • Hands-on experience with GitHub, GitHub Actions, and CI/CD automation.
  • Familiarity with both SQL and NoSQL databases.

Preferred Qualifications

  • Experience with cloud-based GenAI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI).
  • Knowledge of distributed systems, high-availability architectures, and performance tuning.
  • Prior experience leading technical initiatives or architectural decision-making.

Soft Skills

  • Strong leadership, communication, and mentoring capabilities.
  • Ability to work across cross-functional teams in fast-paced Agile environments.
  • Strategic thinking with the ability to translate business needs into scalable AI solutions.

See also

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