AI / ML Engineer
Accenture
- Location
- Onsite (Bengaluru, Karnataka)
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the Role
Accenture is seeking an AI/ML Engineer to embed with client teams and deploy scalable AI platforms in enterprise environments. The role focuses on owning production outcomes end-to-end, translating technical architecture into business impact, and building reusable patterns for client adoption.
Skills
AI Agents
Workflow Integration
Cloud-native Systems
Generative AI
Deep Learning
Neural Networks
Chatbots
Image Processing
Microservices
Containerization
Serverless
RAG
CI/CD
Terraform
Helm
Full job details
Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : AI Agents & Workflow Integration
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Overview :
Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.
Roles & Responsibilities:
Embed directly with client engineering and business teams to deploy, scale, and operationalize AI platforms — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — inside enterprise environments
Own production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestones
Move from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-ready
Design and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integration
Translate technical architecture into business impact for client CTO, CFO, and CISO shape use case roadmaps, ROI backlogs, and AI adoption strategy
Build reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without you
Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teams
Codify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practice.
Professional & Technical Skills:
Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
Experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
Demonstrated end-to-end delivery ownership in a client-embedded environment, internal projects, vendor labs, or team-only deployments do not qualify
Proven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act on
Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level
Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching.
Additional Information:
The candidate should have minimum 5 years of experience in AI Agents & Workflow Integration.3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
All AI Native Engineer foundations confirmed, non-negotiable baseline
Full platform deployment ownership in at least one certified platform in a live client environment
Multi-system integration: connects AI platform to ERP, CRM, ITSM, and data systems in production
Enterprise architecture foundations: identity, data, security, and governance layers, not just application layer
Runs deployment cycles independently, from kickoff to production with client handover
This position is based at our Bengaluru office.
A 15 years full time education is required.
15 years full time education
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : AI Agents & Workflow Integration
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Overview :
Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.
Roles & Responsibilities:
Embed directly with client engineering and business teams to deploy, scale, and operationalize AI platforms — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — inside enterprise environments
Own production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestones
Move from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-ready
Design and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integration
Translate technical architecture into business impact for client CTO, CFO, and CISO shape use case roadmaps, ROI backlogs, and AI adoption strategy
Build reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without you
Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teams
Codify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practice.
Professional & Technical Skills:
Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
Experience deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
Demonstrated end-to-end delivery ownership in a client-embedded environment, internal projects, vendor labs, or team-only deployments do not qualify
Proven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act on
Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level
Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching.
Additional Information:
The candidate should have minimum 5 years of experience in AI Agents & Workflow Integration.3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
All AI Native Engineer foundations confirmed, non-negotiable baseline
Full platform deployment ownership in at least one certified platform in a live client environment
Multi-system integration: connects AI platform to ERP, CRM, ITSM, and data systems in production
Enterprise architecture foundations: identity, data, security, and governance layers, not just application layer
Runs deployment cycles independently, from kickoff to production with client handover
This position is based at our Bengaluru office.
A 15 years full time education is required.
15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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