18 hours ago
Pune, IndiaStaff+
Responsibilities
- Translate business strategy and product goals into technical visions, architecture blueprints, non-functional requirements, and implementation roadmaps.
- Lead stakeholder workshops covering feasibility, scope, solution boundaries, dependencies, and client expectations.
- Design Databricks lakehouse-centric LLM, RAG, agent, model serving, workflow, monitoring, and evaluation architectures.
- Architect model- and tool-agnostic multi-agent systems with orchestration, tool use, memory, context management, MCP/control-plane patterns, and reusable service abstractions.
- Design data and context layers covering ingestion, preprocessing, synchronization, chunking, embeddings, vector search, knowledge graphs, and semantic retrieval.
- Define evaluation frameworks for accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security, and operational reliability.
- Establish AI security, governance, observability, guardrails, prompt-injection defense, PII protection, access control, audit logging, and tracing controls.
- Maintain architecture decision records, component and sequence diagrams, design specifications, integration patterns, and reusable reference architecture assets.
Requirements
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- Minimum 12 years of experience is stated; the detailed qualifications require at least 10 years in software engineering, data engineering, AI/ML engineering, or technology architecture.
- At least 5 years of experience designing and deploying enterprise-grade advanced AI or cloud data solutions using at least one cloud vendor.
- At least 2 years of experience in agentic AI, LLM, and generative AI solution architecture or engineering delivery.
- At least 4 years of coding experience with Python, APIs, distributed systems, reusable frameworks, and cloud-native application patterns.
- At least 4 years of experience in machine learning, deep learning, NLP, data engineering, analytical engineering, or AI product delivery.
- Demonstrated solution or technology architecture experience in industries such as financial services, healthcare, manufacturing, retail, telecom, or life sciences.
- Hands-on experience with Databricks Mosaic AI, Model Serving, Agent Framework, MLflow tracing and evaluation, Vector Search, Unity Catalog, Delta Lake, Delta Live Tables, Lakehouse Monitoring, Feature Store, Workflows, Jobs, and Model Training.
- Strong knowledge of RAG, embeddings, vector databases, prompt engineering, model routing, fine-tuning, function calling, tool integration, and agent orchestration.
- Experience with reusable agent services, memory services, API gateways, integration adapters, orchestration layers, evaluation harnesses, deployment pipelines, CI/CD, infrastructure-as-code, automated testing, MLOps/LLMOps, monitoring, and production release governance.
- Strong stakeholder management and communication skills for explaining architecture trade-offs, risks, and recommendations.
- Preferred experience includes Databricks certifications, Spark/PySpark, Unity Catalog governance, MLflow, LangGraph/LangChain, model fine-tuning, lakehouse optimization, open-source AI frameworks, responsible AI, model risk management, red-teaming, human-in-the-loop review, A/B testing, GenAI FinOps, and enterprise architecture governance.
Tech Stack
Categories
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.