About the Role
We are looking for experienced AI/ML Architects to join our AI Engineering service line. In this role, you will anchor the technical delivery of enterprise AI/Agentic AI projects post deal-closure. You will take over from solution architects and lead the design, build, deployment, and optimization of AI/ML systems — ensuring production-grade quality, scalability, and compliance. You will interface with cross-functional teams, manage engineering complexity, and ensure value realization for customers across industries such as BFSI, HLS, Manufacturing, CMT, Retail, and Energy.
Key Responsibilities
Architecture & Technical Leadership
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Own the end-to-end technical architecture and solution integrity during project delivery.
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Translate solution blueprints into detailed technical designs, backlog, and integration plans.
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Lead detailed design reviews, ensure alignment with AI platform, MLOps, and Agentic AI best practices.
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Select appropriate frameworks, APIs, libraries, and cloud-native services for implementation.
Project Execution & Delivery Oversight
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Serve as technical anchor for customer AI/ML and Agentic AI projects.
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Guide engineering teams on modular, secure, and reusable implementation strategies.
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Review and validate code, infrastructure scripts, and ML pipelines for quality, performance, and reliability.
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Oversee data pipelines, model training, LLMOps, and model deployment workflows.
Hands-on Engineering & Problem Solving
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Provide hands-on support for complex components: LLM pipelines, vector stores, fine-tuning,



evaluations.
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Resolve system integration challenges across APIs, knowledge stores, and orchestration layers.
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Implement or validate MLOps workflows using MLflow, Airflow, Argo, KServe, BentoML, etc.
Quality, Compliance & Observability
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Embed observability, safety, and compliance into the AI/ML lifecycle.
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Integrate with AI governance platforms for model tracking, versioning, audits, and risk controls.
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Ensure alignment with enterprise and regulatory standards like NIST AI RMF, EU AI Act, and internal responsible AI policies.
Collaboration & Customer Engagement
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Act as the technical point of contact for client-side engineering and data science teams during delivery.
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Participate in sprint planning, status reviews, and change control boards.
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Support knowledge transfer, UAT, documentation, and post-deployment handoffs.
Required Qualifications
Education
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B.Tech/M.Tech or equivalent in Computer Science, Data Science, or a related field.
Experience
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8+ years in software architecture or engineering with 5+ years in applied AI/ML system delivery.
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Experience in productionizing AI/ML models and building full-stack AI applications in enterprise settings.
Technical Skills
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Strong Python development skills; proficiency in ML/AI frameworks (PyTorch, TensorFlow, Scikit-learn).
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Solid understanding of LLMs, RAG pipelines, vector databases (Weaviate, Qdrant, Pinecone).
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Experience with MLOps/LLMOps tools: MLflow, Argo, KServe, Feast, Kubeflow.

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