Job Description
Role Summary
We are seeking a Generative AI/ML Engineer to design, build, and deploy intelligent AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and edge computing. You will work hands-on with cloud-native AI services, LLMOps pipelines, and enterprise-grade deployment patterns to solve business problems.
Key Responsibilities
- Design, develop, and fine-tune LLM-powered applications for enterprise use cases.
- Experience in evaluating LLM applications and developing observability frameworks
- Implement RAG pipelines using vector databases, embeddings, and optimized retrieval strategies.
- Build agentic AI workflows with multi-step reasoning, tool calling, and integration with APIs.
- Integrate GenAI solutions into multi-cloud or hybrid cloud environments (AWS, Azure, GCP).
- Develop and optimize edge AI deployments for low-latency use cases.
- Create data strategy, ingestion, transformation, enrichment, validations, quality checks via pipelines for AI ingestion, preprocessing, and governance.
- Implement AI safety, bias mitigation, and compliance measures.
- Work closely with LLMOps teams to enable continuous integration & deployment of AI models.
- Write well-documented, production-ready code in Python, Node.js, Rust .
- Benchmark and evaluate model performance, latency, and cost-efficiency.
Required Skills
- Proficient in cloud AI services (e.g. AWS Bedrock/SageMaker, Azure AI Foundry, Google Vertex AI, Anthropic, OpenAI APIs).
- Strong proficiency with Python and LLM frameworks (e.g. PromptFlow, LangGraph, LlamaIndex, HuggingFace, PyTorch, TensorFlow).
- Hands-on experience with vector DBs (e.g. Pinecone, Weaviate, Milvus, FAISS, Azure Cognitive Search).
- Experience building RAG-based and agentic AI solutions.
- Familiarity with Edge AI frameworks (e.g. NVIDIA Jetson, AWS IoT Greengrass, Azure Percept).
- Multi-modal AI (text, image, speech, video) experience.
- Strong grasp of APIs, microservices, and event-driven architectures.
- Knowledge of AI governance (data privacy, model explainability, security).
- Experience in containerized deployments (Docker, Kubernetes, serverless AI functions).
Preferred Skills
- Generative agents with memory and planning capabilities.
- Real-time AI streaming with WebSockets or Kafka.
- Prior contributions to open-source GenAI projects.
- Experience in build, test and deploy various ML models
- Experience in building MCP, A2A protocol
