Consultant - AI Engineer at KPMG India

Gurgaon, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

18 May, 26

Salary

0.0

Posted On

17 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, R, TensorFlow, FastAPI, LangChain, LangGraph, Docker, Kubernetes, Azure, GCP, AWS, MLOps, GenAI, RAG, Statistical Modeling, API Development

Industry

Business Consulting and Services

Description

Job Description: AI Engineer Roles & responsibilities Model Development & Solution Architecture •Design, build, and optimize machine learning, GenAI, and statistical models to solve complex cross‑domain business challenges. •Develop reproducible, scalable workflows using modern MLOps practices; conduct rigorous model evaluation and continuous improvement for accuracy, reliability, and fairness. •Architect end‑to‑end AI systems including data pipelines, vector databases, orchestration layers, and deployment frameworks. • GenAI, RAG & Agentic Systems •Explore and implement advanced LLM techniques, including embeddings, retrieval‑augmented generation (RAG), agentic workflows, tool use, memory, and multimodal reasoning. •Build production‑grade GenAI applications using LangChain, LangGraph, and ecosystem tools for orchestration, evaluation, and deployment. •Experiment with foundation models across open‑source and commercial platforms to identify best‑fit models for business use cases. • API & Integration Development •Develop and operationalize scalable APIs—preferably with FastAPI (Python)—to integrate ML/GenAI components with enterprise systems, SaaS products, and data platforms. •Collaborate on deployment patterns including microservices, serverless, and containerized workloads (Docker/Kubernetes). • Cloud Platforms & Infrastructure •Architect and deploy AI solutions on Azure, Google Cloud Platform (GCP), or AWS, leveraging managed AI/ML services (Azure ML, Vertex AI, SageMaker), vector search, and serverless compute. •Ensure security, scalability, compliance, and monitoring in all cloud‑based AI deployments. • Consulting, Problem Solving & Stakeholder Collaboration •Partner with product teams, business stakeholders, SMEs, and engineering groups to deeply understand challenges, run discovery workshops, and shape AI strategy and roadmaps. •Translate business requirements into scalable technical architectures, solution designs, and implementation plans. •Communicate complex AI concepts in clear, business‑friendly language and drive adoption of AI‑powered solutions across the organization. Mandatory technical & functional skills •Programming skills: Proficiency in programming languages such as Python and R, and experience with data manipulation libraries (e.g., pandas, NumPy) and machine learning frameworks (e.g., TensorFlow). •Statistical knowledge: Strong understanding of statistical concepts and methodologies such as regression, clustering, hypothesis testing, and time series analysis. •GenAI-based models: Understanding and experience with GenAI-based models, exploring their potential applications, and incorporating them into AI models when appropriate •Communication skills: Strong written and verbal communication skills to effectively present findings, insights, and recommendations to both technical and non-technical stakeholders. •Team player: Proven ability to collaborate effectively with cross-functional teams, take ownership of tasks, and work towards shared goals. •AI passion: Demonstrated interest and enthusiasm for artificial intelligence and a strong desire to grow a career in the AI space •Problem-solving mindset: Ability to think critically and creatively to solve complex business problems using data-driven approaches. Detail-oriented with excellent analytical and problem-solving skills.

Responsibilities

The role involves designing, building, and optimizing machine learning, GenAI, and statistical models to solve complex business challenges, while also architecting end-to-end AI systems including data pipelines and deployment frameworks. Responsibilities also include exploring advanced LLM techniques like RAG and agentic workflows, and developing production-grade GenAI applications.