Architect AI ML

Define end-to-end architecture for AI/ML and Gen AI, Agentic AI, MCP systems including data pipelines, model training/inference, and MLOps.

Serve as a strategic advisor / consultant to clients, leading solution design discussions, presenting AI/ML architectures, and representing 3Pillar in client-facing interactions to drive innovation and business value.

Architect scalable solutions using cloud-native AI tools (Azure ML, AWS SageMaker, or GCP Vertex AI).

Lead the integration of Generative AI into components / features leveraging LLMs into enterprise applications using APIs (OpenAI, Gemini and others) or open source models like LLama.

Design retrieval-augmented generation (RAG) systems with vector databases (Pinecone, Weaviate, FAISS and similar).

Guide teams on MLOps frameworks for CI/CD, model versioning, monitoring, and automated retraining.

Evaluate build-vs-buy decisions and benchmark AI tools/platforms.

Evaluate emerging technologies and trends in AI, ML, Gen AI, Agentic AI space and recommend adoption strategies.

Mentor technical teams and guide solution architects, AI/ML engineers.

Ensure ethical and responsible AI practices including bias detection, interpretability, and governance.

Act as a subject matter expert, providing guidance and mentorship to junior colleagues and strengthen the expertise in the organization.

Collaborate with leadership to align machine learning strategies with overall business objectives.

Represent the organization at industry conferences, workshops, and events.

Drive cross-functional collaboration to identify opportunities for applying machine learning to business challenges.

Master’s degree in Computer Science, Engineering, Mathematics, or a related field with 15+ years of industry experience with strong IT services background in machine learning, data science, AI, with a track record of delivering impactful projects; a Ph.D. is highly desirable.

Strong grasp of AI architecture patterns (RAG, Agent AI, MCP based systems, prompt orchestration).

Deep experience with Python, ML libraries (scikit-learn, XGBoost, PyTorch, TensorFlow).

Hands-on with Gen AI APIs (OpenAI, Claude, Gemini), prompt engineering, embeddings, and fine-tuning.

Experience designing enterprise AI systems with MLOps (MLflow, Kubeflow, SageMaker Pipelines).

Familiarity with APIs, microservices, and containerization (Docker, Kubernetes).

Experience in Data Governance, Model Risk Management, and compliance.

Extensive knowledge of machine learning theory, algorithms, and methodologies.

Strong leadership and communication skills, with the ability to influence stakeholders at all levels of the organization.

Demonstrated ability to think strategically and drive innovation.

Good undertaking of Software Product Development and how to integrate AI ML components into a software product.

Experience in working with huge datasets in a production environment.

Published research in top-tier conferences or journals is a plus.