Description

10+ Years of overall exp and 5+ years of professional software development experience, with strong, current Python expertise.

Demonstrable experience delivering at least one AI/ML or LLM-powered system to production and supporting it through its life cycle.

Practical familiarity with the AI Development Life Cycle (AIDLC): data handling, experimentation, evaluation, deployment, and monitoring.

Hands-on experience with LLM application development — prompt engineering, RAG, and integrating APIs such as those from major model providers.

Proficiency designing and consuming RESTful APIs and building scalable backend services.

Working knowledge with cloud platforms (AWS, GCP, or Azure) and containerization (Docker).

Solid grounding with version control (Git) and CI/CD workflows.

Strong communication skills and the ability to work across engineering, data, and product teams.

 Preferred Qualifications:

Experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, LlamaIndex, or multi-agent frameworks).

Familiarity with vector databases (e.g., Pinecone, Weaviate, pgvector, FAISS) and embedding-based retrieval.

Exposure to MLOps/LLMOps tooling — MLflow, Weights & Biases, model registries, and feature stores.

Experience with Kubernetes and infrastructure-as-code.

Understanding of model evaluation, responsible AI, safety guardrails, and observability for LLM systems.

Background with data pipelines and streaming (e.g., Kafka, Spark, or Airflow).

Core Tech Stack: Python · FastAPI / Flask · LLM & Agentic frameworks · Vector databases · Docker · CI/CD · Cloud (AWS / GCP / Azure) · MLOps/LLMOps tooling

Key Skills

Education

Any Gradute