Python Developer

6 days ago

Bengaluru, India Altysys Full time

JD – Python Developer (GenAI & Agentic AI)Role: Python Developer – GenAI & Agentic AILocation: BangaloreExperience: 5–7 yearsAbout the RoleWe are seeking a highly skilled Python Developer with expertise in Generative AI (GenAI), Agentic AI, and modern LLM (Large Language Model) ecosystems. The ideal candidate will have hands-on experience with LangChain, LangGraph, MCP, AgentOps, RAG pipelines, fine-tuning models, and MLOps practices, along with proficiency in cloud deployment (AWS, Azure AI, Bedrock, etc.). You will be responsible for building, optimizing, and deploying AI-driven solutions that solve real-world business problems at scale.Key Responsibilities- Design & Develop GenAI Applications: Build scalable AI applications using Python, integrating LangChain, LangGraph, MCP, and AgentOps frameworks.- LLM Integration: Work with multiple LLM providers (Azure AI, AWS Bedrock, OpenAI, Anthropic, etc.) for text, multimodal, and agent-based workflows.- RAG Implementation: Architect and deploy Retrieval-Augmented Generation pipelines, integrating vector databases and knowledge graphs.- Fine-tuning & Model Ops: Fine-tune LLMs for domain-specific tasks, implement MLOps pipelines for continuous integration, testing, and monitoring.- Agentic AI Development: Design multi-agent systems with task orchestration, memory handling, and error recovery.- Deployment & Cloud Infrastructure: Deploy applications on AWS cloud (EC2, Lambda, S3, Bedrock, SageMaker, etc.) and Azure AI services.- Performance Optimization: Ensure model efficiency, latency reduction, and cost optimization in production environments.- Collaboration: Work closely with cross-functional teams (Data Scientists, DevOps, Product Owners) to deliver high-quality AI solutions.Required Skills & Qualifications- Strong proficiency in Python with experience in backend development.- Hands-on experience with GenAI frameworks: LangChain, LangGraph, MCP, AgentOps.- Knowledge of RAG (Retrieval-Augmented Generation) pipelines and vector databases (Pinecone, Chroma, Weaviate, FAISS).- Experience in fine-tuning and prompt engineering for LLMs.- Strong understanding of MLOps (CI/CD for ML, model deployment, monitoring).- Experience with cloud AI platforms: Azure AI, AWS Bedrock, AWS SageMaker, GCP Vertex AI (preferred).- Knowledge of Agentic AI concepts – multi-agent orchestration, planning, memory.- Familiarity with Docker, Kubernetes, Terraform, and GitOps practices.- Strong problem-solving and debugging skills.Preferred Qualifications- Prior experience with multi-modal models (text, images, audio).- Exposure to enterprise AI compliance and security best practices.- Familiarity with ISO/IEC AI governance frameworks is a plus.- Open-source contributions in AI/ML projects.

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