Senior Software Engineer

Job Overview

We are seeking a highly skilled and visionary Senior Python Developer / AI Technical Lead with 6 to 7 years of experience to spearhead the design, development, and deployment of our next-generation AI-powered applications.

In this role, you will bridge the gap between robust backend engineering and cutting-edge Artificial Intelligence. You will be responsible for architecting high-performance APIs, implementing complex Retrieval-Augmented Generation (RAG) pipelines, and building autonomous Agentic AI systems. As a technical lead, you will also mentor junior developers, drive best practices, and collaborate closely with product and data science teams to turn AI innovations into production-ready enterprise solutions.

Key Responsibilities

Backend & API Architecture: Design, build, and maintain scalable, high-performance, and secure APIs and microservices using Python and FastAPI.

AI/LLM Integration: Integrate and orchestrate state-of-the-art Large Language Models (LLMs) from commercial providers (e.g., OpenAI, Anthropic) and open-source ecosystems (e.g., Llama, Mistral).

Agentic AI Development: Architect and implement autonomous Agentic AI workflows, multi-agent systems, and tool-use capabilities using frameworks like LangGraph, CrewAI, or AutoGen.

Advanced RAG Pipelines: Design, optimize, and maintain production-grade Retrieval-Augmented Generation (RAG) systems, ensuring accurate context retrieval, data ingestion chunking strategies, and minimized hallucination rates.

Database & Vector Management: Optimize data persistence strategies using structured SQL databases (e.g., PostgreSQL), NoSQL databases (e.g., MongoDB), and specialized Vector Databases (e.g., Pinecone, Milvus, Qdrant, PGVector).

Prompt Engineering & Optimization: Apply advanced Prompt Engineering techniques (e.g., Chain-of-Thought, ReAct, Few-Shot) and utilize programmatic evaluation frameworks to ensure LLM outputs are deterministic, safe, and aligned with business logic.

Technical Leadership: Lead architectural reviews, enforce clean code principles (SOLID, DRY), establish CI/CD pipelines, and mentor a team of engineers in AI engineering best practices.

Required Technical Skills

Core Programming: 6+ years of professional software development experience with expert-level proficiency in Python, asynchronous programming (asyncio), and profiling/optimization.

Frameworks & APIs: Deep hands-on experience building production-ready RESTful and GraphQL APIs using FastAPI; expertise in AI workflow automation using LangGraph, Temporal, Prefect, or Airflow.

Generative AI & LLM Engineering: Expert knowledge of OpenAI, Claude, Gemini, Llama, Mistral, DeepSeek, and cloud platforms (Azure OpenAI, Amazon Bedrock). Skilled in Prompt Engineering, Function/Tool Calling, Structured Outputs, Guardrails, Memory Management, and Eval frameworks (LangSmith, Phoenix, TruLens).

Agentic AI: Proven experience architecting Multi-Agent Systems, Autonomous Workflows, Task Planning, and Graph-based orchestration using LangGraph, CrewAI, AutoGen, or Semantic Kernel.

Enterprise RAG & Search: Mastery in Hybrid/Semantic search and Context Optimization. Proficient with Vector DBs (Pinecone, Weaviate, Milvus, ChromaDB, FAISS) and advanced techniques like Chunking, Embedding optimization, Metadata indexing, Re-ranking, and Citation generation.

OCR & Intelligent Document Processing (IDP): Expertise in building OCR pipelines for layout analysis and extraction (Invoices, PDFs, Tables) using Azure Document Intelligence, AWS Textract, Google Document AI, or open-source tools like PaddleOCR and LayoutLM.

DevOps & Cloud: Strong understanding of Docker, Kubernetes, and cloud infrastructure (AWS/GCP/Azure) to deploy and scale AI workloads and content transformation workflows.

Leadership & Soft Skills

Problem-Solving: Exceptional analytical skills with a knack for debugging complex asynchronous systems and non-deterministic AI outputs.

Mentorship: Proven experience guiding engineering teams, conducting constructive code reviews, and fostering an environment of continuous learning.

Communication: Ability to translate complex technical AI concepts into clear business objectives for non-technical stakeholders.

Agile Mindset: Experienced in working within Agile/Scrum frameworks, managing sprint deliverables, and adapting rapidly to evolving AI technologies.