Artificial Intelligence Engineer

Job Description

Skill

GenAI

Python

MLOps

Retrieval-Augmented Generation (RAG)

Years of Experience

JD

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


• Core AI/ML Foundations:


• 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).


• Generative AI & LLM Expertise:


• 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.


• Strong programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, NumPy, scikit‑learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.


• 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.


• Deployment & MLOps:


• 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, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.


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


• Soft Skills:


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