Roles & Responsibilities
Solution Design
Work closely with CoE leads and department heads to understand requirements and design AI/ML components.
Build rapid PoCs, demos, and prototypes showcasing AI/ML capabilities (GenAI, predictive analytics, computer vision, NLP, etc).
Translate business use cases into ML problem statements, feature sets, and feasible technical approaches.
Closely collaborate with P10 CoE streams and provide necessary support in solving real problems.
Participate in client workshops, discovery sessions, and discussions on technical feasibility studies.
ML Engineering & Development
Develop, train, and deploy machine learning models using modern frameworks (TensorFlow, PyTorch, scikit-learn).
Implement data preprocessing pipelines, feature engineering, and model evaluation.
Build inference pipelines and operationalize models using MLOps best practices.
Optimize model performance, latency, and scalability for production-grade environments.
GenAI & LLM-Based Solutions
Fine-tune and optimize LLMs (eg, Llama, Mistral, GPT-based models) for enterprise use cases.
Build RAG pipelines, vector search solutions, and domain-specific knowledge applications.
Work on prompt engineering, evaluation frameworks, and mechanisms to reduce hallucination.
Integrate GenAI solutions into applications via APIs or containerized deployments.
Data Engineering Collaboration
Work with data engineers to define data requirements, ingestion strategies, and storage architectures.
Ensure data quality, pipeline reliability, and model-ready datasets.
Contribute to building end-to-end data + AI solution blueprints.
Documentation & Knowledge Management
Prepare technical documents, architecture diagrams, and solution design artifacts.
Support the creation of reusable AI assets, accelerators, templates, and best-practice guides.
Technical Skills
- Strong programming experience in Python and ML libraries (NumPy, Pandas, SciPy).
- Hands-on experience with TensorFlow, PyTorch, or JAX.
- Practical experience designing and implementing Agentic AI workflows, autonomous or semi-autonomous AI agents, multi-step reasoning pipelines, and agent orchestration frameworks (LangGraph, CrewAI, AutoGen, etc).
- Experience building RAG, LLM fine-tuning, embeddings, vector databases (Pinecone, FAISS, Weaviate, Chroma).
- Experience deploying models using Docker, Kubernetes, CI/CD, MLflow, or Kubeflow.
Knowledge of cloud AI services:
Azure ML, AWS Sagemaker, Google Vertex AI.
Familiarity with business domains (manufacturing, retail, BFSI, healthcare) is a plus.
Professional Skills
Strong problem-solving and analytical thinking.
Ability to articulate complex concepts in simple language for customers.
Experience in pre-sales, consulting, or solution engineering preferred.
Ability to design PoCs under tight timelines.
