Artificial Intelligence Engineer

Experience

• 8-10 years of relevant experience in Apps Development or systems analysis role

• Strong foundational knowledge in GenAI, Machine Learning (ML modeling), Data Science, Statistics, and AI fundamentals, including Natural Language Processing (NLP), Neural Networks, and Large Language Models (LLMs).

• Extensive hands‑on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open‑source LLMs.
• Critical:
  Deep working knowledge and hands‑on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.
• Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, etc.
• Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates.
• Hands‑on experience with agentic framework‑based use case implementation.
• Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.

• Solid programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, Num Py, scikit-learn, PyTorch, Tensor Flow, Transformers, FastAPI, Seaborn, Lang Chain, and Llama Index.
• Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.
• Hands‑on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.
• Experience in dealing with large amounts of unstructured data and designing solutions for high‑throughput processing.

• Critical:
  Hands‑on experience deploying GenAI‑based models to production environments.
• Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines.
• Strong expertise in CI/CD principles and tools (e.g., Jenkins, Git Lab CI, Azure Dev Ops, ArgoCD) for automated builds, testing, and deployments.

• Proven experience with container orchestration platforms like Open Shift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud‑native environment.

• Strong problem‑solving abilities, excellent collaboration skills for working effectively with cross‑functional teams, and the capability to work independently on complex, ambiguous problems.