Associate Technical Architect - Machine Learning

Role & Responsibilities:
End-to-End Project Delivery: Own the technical delivery of a project from an ML standpoint. Lead the implementation, deployment, and operationalization of ML, Deep Learning, NLP, and Generative AI solutions.
Hands-on Development: Spend 50% to 75% of your time coding. Build robust pipelines, develop advanced agentic workflows, and implement core machine learning components in Python and PyTorch/TensorFlow.
Component-Level Design: Design modular, secure, and scalable AI system components. Create visual system representations (UML, block diagrams, flowcharts) and defend your design choices through rigorous technical reasoning.
Generative AI & Agentic Workflows: Architect and develop advanced Retrieval-Augmented Generation (RAG) pipelines, implement Agentic AI workflows using multi-agent frameworks, and integrate Model Context Protocol (MCP) servers and clients.
MLOps/LLMOps Engineering: Design and maintain production-ready MLOps pipelines (CI/CD, automated testing, model registry, monitoring, retraining frameworks, drift detection) on AWS or GCP.
Technical Mentorship: Code-review and guide senior ML engineers and junior resources, enforcing clean coding standards, modular design patterns, and industry best practices.
Client Engagement: Lead technical discussions with clients regarding project updates, blockers, and architectural decisions. Translate complex technical concepts into clear business impact.
Skills expectation:
Must have:
Experience: 6 to 8 years of professional experience in Machine Learning, Deep Learning, and Software Engineering, with a proven track record of delivering end-to-end ML projects.
Robust Software Engineering:
Exceptional mastery of Python (clean, class-based, modular coding) and SQL for processing complex, large-scale datasets.
Deep understanding of modern software design patterns, Git-based version control, and CI/CD automation.
Advanced ML, DL & NLP:
Extensive hands-on experience in statistical ML (regression, classification, clustering) and Deep Learning architectures (Transformers, CNNs, RNNs).
Solid understanding of NLP concepts (syntactic/semantic parsing, text embeddings, tokenization, NER, coreference).
Generative AI & Agentic Systems (2026 Stack):
Practical experience designing and deploying Generative AI applications and LLM-based solutions.
Hands-on implementation of advanced RAG pipelines and familiarity with Vector Databases (e.g., Pinecone, Milvus, Chroma, Qdrant).
Hands-on experience with Agentic AI Frameworks (e.g., Google ADK, LangChain, LlamaIndex, CrewAI, AutoGen, LangGraph) for autonomous reasoning, planning, and tool use.
Core understanding of Model Context Protocol (MCP) implementations to manage state, memory, and context windows.
AI System Design & Technical Reasoning:
Demonstrated ability to design scalable AI pipelines and systems.
Proficiency in visually diagramming architectures and explaining technical trade-offs with deep, structured reasoning.
Frameworks & MLOps:
Strong proficiency in PyTorch or TensorFlow.
Practical experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker Pipelines, Airflow) and the model lifecycle (feature store, registry, deployment, monitoring).
Good to have:
HCLS Domain Expertise: Previous experience working in the Healthcare & Life Sciences domain (HIPAA, HITRUST compliance, clinical data standards, or digital health systems).
Databricks & PySpark:
Experience using Databricks for collaborative model development and tracking.
Hands-on experience with PySpark or Snowflake for large-scale data processing.
Cloud Certifications: Professional Machine Learning Engineer or Cloud Architect certifications on AWS or GCP or Azure.
Behavioural skills:
Analytical Reasoning: Ability to defend technical decisions, model choices, and architectural components under deep probing (explaining the "why", not just the "how").
Visual Communication: High comfort in using visual design tools to represent system integrations and pipelines clearly.
Client-Facing Presence: Professional, charismatic, and articulate communication style. Ability to lead technical client discussions and manage stakeholder expectations.
Collaborative Leadership: Strong mentorship skills, with a passion for raising the engineering bar and coaching team members.
What is in it for you:
Architectural Ownership: Own the technical architecture and delivery of critical AI initiatives from concept to production.
Sponsored Certifications: Sponsored opportunities to achieve advanced AWS, GCP, Azure, and Databricks professional certifications.
Cutting-Edge Tech: Work on the forefront of AI innovation, including Agentic AI, multi-agent collaboration, and enterprise-scale MLOps.
Accelerated Career Path: Direct exposure to practice leaders and client stakeholders, paving the way to a full Technical Architect role.

Work arrangement
No