AI Engineer

AI Engineer

Technical Leadership & System Architecture

Lead the architecture and development of LLM-driven applications, AI agents, and RAG-based systems.

Provide technical guidance, conduct code reviews, and mentor junior team members.

Drive best practices in Python backend engineering, API development, and AI system design.

Backend Engineering (Python)

Build and maintain backend services using FastAPI or Flask.

Develop scalable API endpoints for AI applications, embeddings, and retrieval systems.

Ensure backend code quality, modularity, performance, and maintainability.

LLMs, RAG, and AI Agent Development

Build AI applications using:
Lang Chain, Lang Graph, Semantic Kernel, Haystack, Llama Index, Auto Gen

Develop autonomous or semi-autonomous AI agents with tool calling and workflow graphs.

Implement Retrieval-Augmented Generation (RAG), embedding pipelines, chunking strategies, reranking, and grounding techniques.

Work with OpenAI SDK and other LLM providers (Anthropic, Azure OpenAI, Cohere, etc.).

Manage prompt engineering, prompt routing, safety guardrails, and evaluation metrics.

Data & Vector Search Engineering

Build data pipelines for indexing, embeddings, and retrieval workflows.

Work with SQL databases (PostgreSQL, MySQL, etc.) for metadata and application storage.

Work with vector databases such as:
Redis, Postgres with pgvector, Elasticsearch, Neo4j, or others.

Implement and optimize search workflows using FAISS or similar similarity search libraries.

MLOps, Deployment & Observability

Deploy AI services using Docker, container orchestration, and cloud environments.

Implement monitoring for AI behavior, performance, error rates, and retrieval accuracy.

Set up CI/CD pipelines for backend and AI components.

Optimize inference cost, latency, and reliability.

Cross-Functional Collaboration

Collaborate with product, data engineering, and business teams to understand requirements.

Translate business problems into scalable AI architectures and deliver practical solutions.

Communicate technical decisions, trade-offs, and progress to stakeholders.

Requirements:

Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or related fields.

10+ years of experience in Python backend development.

Strong proficiency in FastAPI or Flask.

Strong working knowledge of SQL databases (Postgres, MySQL, etc.).

Hands-on expertise with vector databases:
Redis, Postgres/pgvector, Elasticsearch, or Neo4j.

Practical experience with FAISS for similarity search.

Hands-on experience with modern LLM frameworks:
Lang Chain, Lang Graph, Semantic Kernel, Haystack, Llama Index, Auto Gen.

Strong understanding of: embeddings & vector search, RAG pipelines, retrieval optimization, chunking strategies, document loaders & indexing.

Experience building AI apps using OpenAI SDK or similar.

Experience deploying APIs/services using Docker and cloud environments.

Leadership experience: guiding teams, conducting reviews, driving architecture decisions.