AI/ML Architect

Dear,

Greetings..!!

We are looking for AI/ML Architect role for our organization.

Role: AI/ML Architect

Exp: 10+ Years

Location: Hyderabad (On-Site)

Availability: Immediate

Job Description:

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

• Own the end-to-end technical architecture and solution integrity during project delivery.
• Translate solution blueprints into detailed technical designs, backlog, and integration plans.
• Lead detailed design reviews, ensure alignment with AI platform, MLOps, and Agentic AI best practices.
• Select appropriate frameworks, APIs, libraries, and cloud-native services for implementation.
• Project Execution & Delivery Oversight
• Serve as technical anchor for customer AI/ML and Agentic AI projects.
• Guide engineering teams on modular, secure, and reusable implementation strategies.
• Review and validate code, infrastructure scripts, and ML pipelines for quality, performance, and reliability.
• Oversee data pipelines, model training, LLMOps, and model deployment workflows

🧠

Hands-on Engineering & Problem Solving

• Provide hands-on support for complex components: LLM pipelines, vector stores, fine-tuning, evaluations.
• Resolve system integration challenges across APIs, knowledge stores, and orchestration layers.
• Implement or validate MLOps workflows using MLflow, Airflow, Argo, KServe, BentoML, etc.

🔍 Quality, Compliance & Observability

• Embed observability, safety, and compliance into the AI/ML lifecycle.
• Integrate with AI governance platforms for model tracking, versioning, audits, and risk controls.
• Ensure alignment with enterprise and regulatory standards like NIST AI RMF, EU AI Act, and internal responsible AI policies.

🔄 Collaboration & Customer Engagement

• Act as the technical point of contact for client-side engineering and data science teams during delivery.
• Participate in sprint planning, status reviews, and change control boards.
• Support knowledge transfer, UAT, documentation, and post-deployment handoffs.

Required Qualifications

🎓 Education :

B.Tech/M.Tech or equivalent in Computer Science, Data Science, or a related field.

Experience

• 8+ years in software architecture or engineering with 5+ years in applied AI/ML system delivery.
• Experience in productionizing AI/ML models and building full-stack AI applications in enterprise settings.
• Strong Python development skills; proficiency in ML/AI frameworks (PyTorch, TensorFlow, Scikit-learn).
• Strong understanding of LLMs, RAG pipelines, vector databases (Weaviate, Qdrant, Pinecone).
• Experience with MLOps/LLMOps tools: MLflow, Argo, KServe, Feast, Kubeflow.
• Proficiency in data pipeline engineering using Spark, Airflow, or DataFlow.
• Exposure to agent orchestration frameworks: LangChain, LangGraph, AutoGen, CrewAI is a big plus.
• Cloud & Infrastructure
• Hands-on experience with GCP (Vertex AI, BigQuery, Document AI, AI Gateway) and/or Azure (Azure ML, OpenAI, Synapse).
• Expertise in containerization (Docker) and orchestration (Kubernetes).
• Familiarity with Infrastructure as Code (Terraform, Pulumi, CDK).

Soft Skills

• Strong architectural thinking and problem-solving in fast-paced delivery environments.
• Excellent communication and collaboration skills to work across cross-functional teams and clients.
• Proactive, structured, and detail-oriented with a bias for execution.
• Nice to Have
• Experience in real-world deployments of Agentic AI systems or collaborative multi-agent setups.
• Exposure to regulatory/ethical concerns in AI such as fairness, transparency, or bias mitigation.
• Familiarity with AI observability, explainability, and governance tooling (e.g., Arize, Fiddler, TruEra).