28 days ago
Bhubaneshwar, IndiaStaff+
Responsibilities
- Design end-to-end AI architecture covering data, models, agent orchestration, and application integration.
- Define multi-agent collaboration, tool use, workflow orchestration, memory, planning, reasoning, and feedback-loop architectures.
- Architect Generative AI solutions using LLMs, VLMs, multimodal models, prompt strategies, RAG pipelines, embeddings, and semantic indexing.
- Establish approaches for LLM evaluation, grounding, hallucination mitigation, explainability, safety, cost efficiency, and guardrails.
- Provide architectural oversight for regression, classification, clustering, anomaly detection, forecasting, time-series, and predictive analytics systems.
- Guide feature engineering, model selection, validation, performance metrics, model drift, data drift, and retraining strategies.
- Design scalable data architectures involving data lakes, lakehouses, feature stores, vector databases, and knowledge graphs.
- Architect MLOps and LLMOps frameworks covering model and prompt versioning, lineage, monitoring, observability, alerting, and deployment practices.
- Define AI governance for security, privacy, compliance, auditability, explainability, responsible AI, and regulatory alignment.
- Integrate AI systems with enterprise IT and OT systems such as ERP, historians, MES, and CRM platforms.
- Support solution blueprinting, technical design reviews, architecture sign-offs, proofs of concept, pilots, and scaled deployments.
- Mentor engineers, data scientists, and technical teams while influencing technical direction and AI platform evolution.
Requirements
- At least 12 years of overall experience in software engineering, data, and AI systems.
- At least 5 years of hands-on experience with machine learning or AI solutions in production.
- At least 3 years of practical experience with Generative AI and/or Agentic AI systems.
- Strong architectural thinking, abstraction, solution-design, stakeholder communication, and technical storytelling skills.
- Proven ability to influence technical direction at scale and work in ambiguous, evolving problem spaces.
- Experience with Accenture Delivery Architectures (ADA).
- Experience with industrial, manufacturing, energy, or enterprise AI use cases is preferred.
- Exposure to optimization, simulation, or decision-support systems is preferred.
- Contributions to internal AI platforms, accelerators, or reusable frameworks are preferred.
- 15 years of full-time education is required.
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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.