Your Team's Impact We are looking for a Senior Software Engineer who is passionate about Generative AI, Agentic systems, and cloud-native API development. In this role, you will be a key contributor in designing, building, and deploying production-grade AI-powered applications and intelligent agent frameworks that transform the way our products and customers interact with data and automation. You will collaborate closely with ML Engineers, Data Scientists, Product Managers, and Cloud Architects to deliver scalable, reliable, and innovative solutions powered by cutting-edge GenAI technologies. What You'll Do GenAI & Agentic Development Design and develop Agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI , or similar Build and integrate LLM-powered applications using models from OpenAI, Anthropic, Meta (LLaMA), Mistral , or foundation models on cloud platforms Implement Retrieval-Augmented Generation (RAG) pipelines, vector stores, and semantic search capabilities Develop multi-agent orchestration systems with defined tool use, memory management, and reasoning loops Evaluate, fine-tune, and optimize LLM behavior through prompt engineering and chain-of-thought design API Development & Backend Engineering Build high-performance, scalable RESTful APIs and async microservices using FastAPI and Python Design clean, modular, and well-documented API contracts following OpenAPI/Swagger standards Implement authentication, authorization, rate limiting , and security best practices across APIs Develop event-driven and streaming architectures to support real-time AI inference pipelines Cloud & Platform Engineering Deploy and manage AI workloads on AWS (SageMaker, Lambda, API Gateway, Bedrock, S3, ECS/EKS) Utilize Databricks for large-scale data processing, MLflow model tracking, and AI/ML pipeline orchestration Build CI/CD pipelines and infrastructure-as-code (IaC) using tools like Terraform, AWS CDK , or similar Ensure scalability, observability, and reliability through cloud-native monitoring and logging practices Engineering Excellence Write clean, testable, production-ready Python code following software engineering best practices Conduct code reviews and mentor junior engineers on GenAI patterns and best practices Contribute to architectural decisions, technical roadmaps , and engineering documentation Stay current with the rapidly evolving GenAI ecosystem and champion the adoption of emerging tools What We're Looking For 57 years of professional software engineering experience Hands-on Python development in production environments Experience with GenAI technologies including LLMs, RAG, embeddings, or prompt engineering Building and deploying APIs using FastAPI or similar async Python frameworks Experience working with AWS cloud services in a production environment Hands-on experience with Databricks for data/AI workloads Python & Frameworks Expert-level Python (async programming, type hints, Pydantic, dependency injection) FastAPI – routing, middleware, background tasks, OpenAPI spec generation Experience with SQLAlchemy, Alembic , or async ORMs Unit/integration testing with pytest, pytest-asyncio GenAI & Agentic Strong understanding of LLM internals , tokenization, context windows, and model behavior Experience with LangChain / LangGraph / AutoGen / CrewAI or similar agentic frameworks Familiarity with vector databases – Pinecone, Weaviate, pgvector, ChromaDB, or FAISS Knowledge of RAG architectures , chunking strategies, and embedding models Hands-on experience with OpenAI API, Anthropic Claude API, AWS Bedrock , or Hugging Face models Cloud – AWS AWS Bedrock – LLM model invocation and management AWS Lambda, API Gateway, ECS/EKS – serverless and containerized deployments S3, DynamoDB, RDS/Aurora – data storage patterns IAM, VPC, Secrets Manager – security and access control Familiarity with AWS CDK or CloudFormation Databricks Experience with Databricks Workflows, Delta Lake, Unity Catalog Familiarity with MLflow for experiment tracking and model registry Using Databricks for AI/BI or LLM serving pipelines General Engineering Docker & Kubernetes – containerization and orchestration Git – branching strategies, PR workflows, GitOps CI/CD – GitHub Actions, GitLab CI, Jenkins, or similar Understanding of distributed systems , event streaming (Kafka or AWS Kinesis), and microservice patterns Preferred / Nice-to-Have Qualifications Experience with multi-modal AI (vision + language models) Familiarity with Azure OpenAI Service or Google Vertex AI Exposure to Terraform or Pulumi for infrastructure automation Contributions to open-source AI/ML projects Experience with GraphQL APIs Background in data engineering or MLOps pipelines AWS Certifications ( Solutions Architect, ML Specialty ) Databricks Certified Associate or Professional badge Education Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related technical field Equivalent practical experience and demonstrable project portfolio will be considered.