Senior AI & Python Technical Lead

About the Company

Role Overview

Responsibilities

• Lead and mentor AI, Python, ML, data, MLOps, cloud, and solution architecture professionals.
• Own technical architecture, technology strategy, engineering standards, and solution delivery.
• Drive AI innovation, proofs of concept, reusable frameworks, enterprise AI adoption, and platform modernization.
• Lead cross-functional delivery with business stakeholders and establish best practices across AI, software engineering, DevOps, cloud, and MLOps.

Candidate Profile

• Bachelor's or Master's degree in computer science, AI, ML, data science, software engineering, or a related discipline.
• At least 10 years of hands-on Python software engineering and enterprise application development experience.
• At least five years designing, developing, and deploying production-grade AI/ML and generative AI solutions.
• At least three years architecting RAG, Agentic AI, and enterprise AI platforms.
• Experience leading technical teams as a Technical Lead, AI Engineering Manager, Principal Engineer, or Solution Architect.
• Experience delivering enterprise AI platforms on AWS or another major cloud provider.
• Strong stakeholder management, engineering governance, solution architecture, and large-scale Agile delivery experience.

Programming and Backend

• Python, SQL, and NoSQL are listed for programming and backend development.
• The technical stack includes FastAPI, Flask, Django, REST APIs, GraphQL, microservices, and distributed systems.

AI and Generative AI

• The AI stack includes PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, LangChain, LlamaIndex, and DSPy.
• Generative AI capabilities include LLMs, RAG, GraphRAG, AI agents, multi-agent systems, prompt engineering, embeddings, semantic search, and hybrid search.
• Agent frameworks include LangGraph, CrewAI, Microsoft AutoGen, and LangChain Agents.

Platforms, Operations, and Governance

• Listed vector databases include Pinecone, Qdrant, Weaviate, ChromaDB, Milvus, and FAISS.
• LLMOps and evaluation tools include LangSmith, Ragas, DeepEval, OpenAI Evals, and MLflow, with model monitoring and AI observability.
• AI security topics include guardrails, prompt injection protection, PII protection, responsible AI, and AI governance.
• Cloud and operations technologies include AWS Bedrock, SageMaker, Docker, Kubernetes, Terraform, Jenkins, GitHub Actions, ArgoCD, CI/CD, GitOps, and infrastructure as code.