AI/ML Engineer

TITLE - AI/ML Engineer (Junior–Mid Level) Location: On-site - Jaipur Employment Type: Full-Time About the Role We are looking for a hands-on AI/ML Engineer to help design, build, and ship enterprise AI solutions. You will work with large language models (LLMs), inference APIs, and modern AI application frameworks to deliver production-grade capabilities — from intelligent assistants and agentic workflows to retrieval-augmented generation (RAG) pipelines. This role is ideal for an engineer who is strong in Python, curious about the rapidly evolving GenAI ecosystem, and eager to grow into advanced model development.

Must Have Python Programming — Strong proficiency in Python, including writing clean, modular, production-quality code; familiarity with common libraries (Pandas, FastAPI/Flask, Pydantic, async patterns). LLM Inference APIs — Hands-on experience integrating and orchestrating LLMs via inference APIs (OpenAI, Anthropic Claude, AWS Bedrock, Azure OpenAI, Google Vertex AI/Gemini, or similar), including prompt engineering, function/tool calling, and structured outputs. AI Application Patterns — Practical experience building GenAI applications such as RAG pipelines, embeddings/vector search (e.g., FAISS, Pinecone, pgvector), or chat/agentic workflows using frameworks like LangChain, LlamaIndex, or native SDKs.

Nice to Have Building Custom LLMs/SLMs — Experience fine-tuning or training custom LLMs/small language models (LoRA/QLoRA, PEFT, Hugging Face Transformers), model quantization, or self-hosted inference (vLLM, Ollama, TGI). Cloud & MLOps — Deploying AI workloads on Azure, AWS, or GCP (SageMaker, Azure ML, Vertex AI, Databricks), containerization (Docker/Kubernetes), and model monitoring/observability. Agentic AI & Emerging Standards — Exposure to multi-agent frameworks, Model Context Protocol (MCP), evaluation frameworks, or responsible AI/guardrails tooling. Education Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).

Key Responsibilities Develop and maintain AI-powered applications and services in Python, integrating with LLM inference APIs (e.g., OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Cloud Vertex AI/Gemini). Build and optimize prompt pipelines, RAG workflows, and structured-output patterns for enterprise use cases. Implement API integrations, data preprocessing, and evaluation harnesses to measure model quality, latency, and cost. Collaborate with architects, product leads, and data engineers to translate business requirements into working AI solutions. Follow engineering best practices: version control, code reviews, testing, CI/CD, and secure handling of data and credentials. Stay current with the GenAI landscape (models, frameworks, tooling) and bring recommendations to the team.

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