AI/ML Architect
First seen by Alion on Sep 3, 2026.
Job Description :
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 :
- 10+ 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).
Skills
AI Architecture, Artificial Intelligence, Machine Learning, Agentic AI, MLOps, LLM, RAG, PyTorch, Tensorflow, Apache Airflow, LangGraph