USI EH27 - Consulting-RRF | EOR - IPAS | Consultant
Deloitte
Position Summary
Regulatory, Risk & Forensic
Our Regulatory, Risk & Forensic team supports client leaders to translate multifaceted risk and an evolving regulatory environment into defensible actions that strengthen, protect, and transform organizations. Leverage advanced data, AI, and emerging technologies with deep domain and industry insights to help bring clarity from complexity and accelerate the path to value creation.
Position Summary
Level: Consultant
As an experienced Consultant at Deloitte Consulting Services, you will be responsible for individually delivering high quality work products within due timelines. Need-basis you will be mentoring and/or directing junior team members/liaising with onsite/offshore teams to understand the functional requirements.
Work you’ll do:
- Collaborate with technical teams and senior management stakeholders to understand requirements for AI/ML solutions, including engineering constraints (safety, reliability, latency, cost, regulatory).
- Partner with engineering, product, and broader technology teams to implement AI/ML, GenAI and Agentic AI solutions and services across the lifecycle.
- Participate and contribute to the creation of proofs of concept (PoCs) as part of pursuit / sales cycles, including engineering value cases (e.g., predictive maintenance, yield improvement, warranty reduction).
- Identify opportunities to apply the latest advancements in AI/ML to build, test, and validate predictive and prescriptive models for industrial/engineering use cases (e.g., anomaly detection, Remaining Useful Life (RUL), quality prediction).
- Design and develop algorithms and automated processes for model validation, deployment, monitoring, and continuous improvement, with emphasis on verification & validation and traceability.
- Identify data, sensor/edge, technology, integration, cybersecurity, and operational requirements for implementing AI/ML models (including IoT data pipelines and historian/SCADA integrations where applicable).
- Work with clients and internal stakeholders to research, access, and procure data required for training models, including time-series and sensor data, maintenance logs, quality records, and engineering metadata.
- Contribute to the creation and maintenance of documentation, user guides, and training materials, including model cards, data lineage, validation reports, and operational runbooks.
- Support research and development efforts to understand and apply the latest advancements in AI/ML technologies, including Digital Twin–enabled analytics and physics-informed approaches where appropriate.
- Collaborate on AI risk management and governance: define controls for model risk, explainability, robustness, bias/fairness, privacy, security, change management, approvals, and audit readiness.
- Incorporate product safety and quality considerations into solution design: safe-fail behavior, human-in-the-loop decisioning, escalation workflows, and alignment to client quality processes.
Our Enterprise Performance team is at the forefront of enterprise package insights and platform technology, working across finance, supply chain, manufacturing, and IT operations to support delivery of holistic performance improvement and package led large scale digital transformation. Join our team of strategic advisors and architects, differentiated by our industry depth to help collaborate and leverage your experience in strategy, process design, technology enablement, and operational services to enable the heart of business insights and collaborate with the businesses as they navigate their enterprise growth and transformation journeys.
Qualifications
Must Have Skills/Project Experience/Certifications:
- Experience: 2 years to 4 years (with 2+ years of experience in AI/ML technologies).
- Bachelor’s or master’s or advanced certifications in Information Technology, Engineering, Statistics, Data Science, Applied Mathematics, Computer Science, Physics, and related fields.
- Intermediate understanding of at least one programming language and AI frameworks/algorithms.
- Knowledge of Deep Learning frameworks such as TensorFlow, Keras, or PyTorch.
- Hands-on experience working with Generative AI (GenAI) models and/or Large Language Models (LLMs).
- Demonstrated ability to apply structured problem-solving to complex, real-world engineering/operational issues.
- Excellent verbal and written communication skills, including ability to explain technical outcomes to non-technical stakeholders.
- Engineering data readiness: familiarity with messy, real-world datasets (sensor noise, missing data, drift) and practical preprocessing for time-series/industrial data.
- Basically, model operationalization: understanding of deployment patterns (batch, streaming, API) and production monitoring concepts (data drift, model drift, performance decay).
Good to Have Skills/Project Experience/Certifications:
- IoT and edge analytics: working with sensor telemetry, streaming pipelines, edge constraints, and industrial connectivity concepts (e.g., gateways, message brokers).
- Digital Twins: familiarity with digital twin concepts and architectures (asset models, state estimation, simulation + data-driven hybrid approaches) and applying twins for monitoring/diagnostics.
- Predictive Maintenance exposure: time-series forecasting, anomaly detection, RUL modeling, maintenance strategy alignment, and evaluation in operational settings.
- Product safety, quality, and reliability analytics: applying AI/ML to quality inspection, process capability, root-cause analysis, and reliability/warranty signals; familiarity with quality methods (e.g., SPC concepts) is a plus.
- AI risk management & governance: experience with model risk controls (documentation, approvals, monitoring, explainability, robustness testing), and supporting audit-ready artifacts (model cards, validation reports, lineage).
- Deep knowledge of common programming and scripting languages such as Python (preferred), R, Java, C++.
- Familiarity with cloud computing services (Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure).
- Understanding of best practices in Machine Learning and experience working in an agile environment.
- Knowledge of techniques for managing and optimizing inference costs and latency for large models.
- Experience integrating LLMs / GenAI APIs (e.g., OpenAI, Hugging Face, Azure OpenAI Service) into production systems.
- BE/B.Tech/M.C.A./M.Sc (CS) degree or equivalent from accredited university
- Bengaluru/Hyderabad
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Requisition code: 351430