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.