AI / ML Engineer
Accenture in India · Bengaluru, Karnataka, India
- On site
- Full-time
- ₹2,000,000 / year
- Bengaluru, Karnataka, India
Job highlights
- Design, build, and deploy LLM-powered AI systems.
- Develop agentic systems with orchestration and planning.
- Implement RAG pipelines and integrate vector databases.
- Deploy AI services on cloud platforms.
- Rapidly prototype and iterate on AI solutions.
About the role
AI / ML Engineer
Accenture in India is seeking a hands-on AI Native Engineer to design, build, and deploy end-to-end agentic and LLM-powered systems. This role involves constructing RAG pipelines, working with vector databases, building autonomous or semi-autonomous agents, integrating tools and skills, and developing evaluation frameworks for high-quality AI behavior. You will prototype rapidly, experiment with models, and evolve solutions from PoC to production while working closely with cross-functional teams.
Key Responsibilities
Agentic & LLM System Development
- Build agentic AI systems, including agent orchestration, planning loops, tool calling, and memory modules.
- Implement LLM toolchains, custom prompts, templates, evaluators, and multi-step reasoning workflows.
- Develop autonomous/semi-autonomous agents for retrieval, summarization, decision support, or workflow automation.
RAG Pipelines & Vector Intelligence
- Design and implement RAG pipelines end-to-end: ingestion, chunking, embeddings, indexing, vector search, hybrid retrieval, and grounding.
- Integrate vector databases such as pgvector, Pinecone, Weaviate, or Milvus.
- Optimize retrieval quality, latency, and factual accuracy using rerankers, retrieval evaluators, and freshness pipelines.
Model Integration & AI Ops
- Integrate enterprise-grade AI APIs, foundation models, and transformer models into scalable systems.
- Implement robust evaluation frameworks including offline and online evals, regression tests, content safety checks, and red-team scenarios.
- Build monitoring for model drift, agent failure modes, hallucination detection, and end-to-end system health.
Full-Stack & Cloud Alignment
- Deploy services using Python/Node/Java microservices, serverless functions, or containerized workloads.
- Integrate event streams, API gateways, and cloud-native patterns across Azure/AWS/GCP.
- Build CI/CD pipelines for AI services with safe rollouts, versioning, and feature flags for model updates.
Prototyping & Rapid Iteration
- Rapidly experiment with models, embeddings, architectures, and agentic patterns.
- Translate business requirements into AI-native technical architectures and communicate trade-offs via demos and deep-dives.
- Document designs, experiments, and evaluation results for reproducibility and knowledge sharing.
Key skills/competency
- Machine Learning
- AI Native Engineer
- Agentic AI Systems
- LLM
- RAG Pipelines
- Vector Databases
- Prompt Engineering
- Cloud AI
- Python
- Production Deployment
Skills & topics
- AI Engineer
- ML Engineer
- Machine Learning
- Artificial Intelligence
- LLM
- Generative AI
- RAG
- Vector Databases
- Python
- Cloud AI
- Accenture
- India
How to get hired
- Tailor your resume: Highlight specific AI/ML engineering experience, LLM, RAG, and agentic system development.
- Showcase Python skills: Emphasize projects involving data pipelines, AI services, and evaluation harnesses.
- Detail production experience: Include examples of deploying AI systems with monitoring and error handling.
- Prepare for technical interviews: Be ready to discuss transformer models, prompt engineering, and evaluation frameworks.
- Understand Accenture's culture: Research their focus on AI and innovation to align your application.
Technical preparation
Master Python for AI/ML development.,Study LLM, RAG, and agentic frameworks.,Practice prompt engineering and fine-tuning.,Prepare for production deployment scenarios.
Behavioral questions
Describe a complex AI project you led.,How do you handle model drift or failure?,Explain your approach to rapid prototyping.,How do you ensure AI system reproducibility?
Frequently asked questions
- What is the minimum experience required for the AI / ML Engineer role at Accenture in India?
- The role requires a minimum of 5 years of experience in ML/AI engineering, with a preference for AI-native experience. The job description also mentions a minimum of 3 years of experience in ML/AI engineering specifically.
- What are the key technologies and frameworks for the AI / ML Engineer position at Accenture?
- Key technologies include LLMs, agent frameworks like LangChain, LlamaIndex, Semantic Kernel, LangGraph, AutoGen, and vector databases such as pgvector, Pinecone, Weaviate, or Milvus. Strong Python skills are essential for building agents, RAG services, and data pipelines.
- What educational background is expected for this AI / ML Engineer job?
- The role requires 15 years of full-time education, which typically aligns with a Bachelor's or Master's degree in a relevant field.
- Does this AI / ML Engineer role involve cloud platforms?
- Yes, experience with cloud AI platforms like Azure OpenAI, AWS Bedrock, or Google Cloud Vertex AI is considered a 'good to have' skill. Deployment will involve integrating with cloud-native patterns across Azure, AWS, and GCP.
- What are the primary responsibilities of an AI Native Engineer at Accenture?
- The primary responsibilities include designing and building agentic AI systems and LLM-powered solutions, implementing RAG pipelines, integrating vector databases, developing evaluation frameworks, and deploying these AI services to production.
- Is this AI / ML Engineer role in India a remote position?
- The job description explicitly states the location as Bengaluru, India, indicating it is an on-site position.
- What kind of AI models and techniques will be used by the AI / ML Engineer?
- The role involves working with foundation models, transformer models, deep learning, neural networks, chatbots, image processing, and potentially multimodal models and advanced reasoning agents.
- How important is production deployment experience for this AI / ML Engineer role?
- Experience deploying AI systems to production with monitoring, error handling, retries, and fallback strategies is a 'must-have' skill. Building CI/CD pipelines for AI services is also a key part of the role.