{"id":2477252,"url":"https://alion.io/job/huron-ai-engineer","title":"AI Engineer","company":{"id":3031,"name":"Huron","domain":"huronconsultinggroup.com","url":"https://alion.io/company/huron","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Noida, India","Chennai, India","Pune, India","Jaipur, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":49000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CrewAI","optional":false},{"name":"Edge AI","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Ignite","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true}],"status":"live","first_seen_at":"2026-10-13T21:45:46Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T02:04:43Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future. \nJoin our team as the expert you are now and create your future.\nWe are seeking an experienced AI/ML Engineer with expertise in both machine learning (ML) and Generative AI (GenAI) to join our Emerging Technology Practice. In this role, you will drive the development of cutting-edge AI models and solutions-from statistical modeling and predictive analytics to advanced generative and agentic applications.Design and develop AI-powered systems using both traditional ML and Generative AI techniques, including prompt engineering, fine-tuning, embeddings, and Retrieval-Augmented Generation (RAG) architectures\nBuild intelligent applications and agentic systems that can perform tasks autonomously or interactively, using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar\nArchitect and implement end-to-end RAG pipelines, including document ingestion, chunking strategies, embedding generation, vector indexing, hybrid search, and contextual response generation\nDevelop modular, well-documented APIs and service components (e.g., using FastAPI or Flask) to enable model integration and consumption\nDesign, build, and manage end-to-end AI pipelines, including data ingestion, transformation, validation, model training, inference, deployment, and knowledge retrieval workflows\nLeverage AWS and Azure AI/ML services (e.g., Bedrock, SageMaker, Azure OpenAI, Azure ML, Document Intelligence, vector databases, etc.) to accelerate development and deployment\nImplement observability, evaluation, and monitoring mechanisms to track model accuracy, drift, retrieval quality, grounding fidelity, reliability, and task-level reasoning\nOptimize RAG systems for latency, scalability, cost efficiency, and response quality in production environments\nCollaborate with software engineers, data scientists, solution architects, and business stakeholders to ensure seamless solution design and transition to production\nSupport both rapid experimentation (POCs, pilots) and robust, scalable deployments, depending on the maturity of each use case\nStay up to date with emerging trends in Generative AI, AI agents, orchestration frameworks, RAG evaluation techniques, and continuous improvement methodologies\n4 to 6 years of hands-on experience in AI/ML development or intelligent application engineering\nStrong proficiency in Python and familiarity with modern ML/AI libraries (e.g., scikit-learn, PyTorch, TensorFlow, OpenAI APIs)\nHands-on experience designing and deploying RAG-based systems, including embeddings, vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search), and retrieval optimization techniques\nExperience building APIs or backend services using FastAPI, Flask, or equivalent frameworks\nExposure to agent frameworks (e.g., LangGraph, AutoGen) and multi-agent orchestration patterns\nExperience with orchestration standards or tools such as MCP or agent routing protocols is a plus\nFamiliarity with cloud platforms (AWS, Azure) and containerization tools (Docker)\nHands-on experience with cloud-based ML platforms such as Azure Machine Learning, AWS SageMaker, or Google AI/ML services\nExperience with MLOps pipelines, CI/CD for model delivery, RAG evaluation frameworks (e.g., RAGAS), or model monitoring is a plus\nKnowledge of AI system evaluation, observability, prompt performance testing, and grounding validation is a plus\nAbility to work on cross-functional teams, balance multiple projects, and communicate effectively with technical and non-technical audiences\nCloud certifications in AI/ML services (Azure, AWS, or Google Cloud) are a plus\nBachelor’s degree in Computer Science, Engineering, Data Science, or a related field\nPosition Level\nAssociateCountry\nIndia","description_format":"text","description_chars":4022,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Data & Analytics","Government","Professional 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