Senior Software Engineer - Gen AI

Your Team's Impact

Senior Software Engineer

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.