{"id":2925047,"url":"https://alion.io/job/larsen-toubro-ai-engineer-agentic-gen-ai","title":"AI Engineer (Agentic / Gen AI)","company":{"id":2124,"name":"Larsen & Toubro","domain":"larsentoubro.com","url":"https://alion.io/company/larsentoubro","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":93,"open_postings":11,"ghost_share":0,"stale_share":0.273,"repost_share":0,"time_to_fill_p50_days":47,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":45000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":32},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWQ","optional":false},{"name":"Context Engineering","optional":false},{"name":"Edge AI","optional":false},{"name":"Embeddings","optional":false},{"name":"Federated Learning","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GPTQ","optional":false},{"name":"GraphRAG","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Llama","optional":false},{"name":"LoRA","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Neo4j","optional":false},{"name":"ONNX","optional":false},{"name":"OpenVINO","optional":false},{"name":"PEFT","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Sensor Fusion","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorRT","optional":false},{"name":"Transformers","optional":false},{"name":"Digital Twin","optional":true}],"status":"live","first_seen_at":"2026-10-15T00:00:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-11-06T03:26:49Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"Job Title: Applied Research Engineer, AI (EPC & Manufacturing)Focus areas : Edge AI, Agentic AI\nLocation: Chennai, Tamil Nadu\nJob Summary:\nYou'll lead applied research in creating and optimizing small language models (SLMs) and derived models\nfor range of use cases cutting across both structured and unstructured data - for edge AI deployment,\npowering agentic systems, digital threads, and highly contextual knowledge graphs. Focus on real-world\nEPC/manufacturing scenarios like autonomous workflow orchestration, connected operations, and aimed\nat taking digital transformation in these sectors to the next generation of technologies and with\nbreakthrough impact.\nKey Responsibilities:\nDesign, train, and distill SLMs or derived models (e.g., from Llama/Mistral) for domain-specific\ntasks in EPC/manufacturing, with deeper context engineering and mature agentic AI workflows,\noptimizing for low-latency inference on edge devices for site level intelligence and predictability.\nArchitect agentic AI frameworks that leverage SLMs for autonomous decision-making, such as\nmulti-agent orchestration for construction sequencing or factory floor optimization.\nBuild and integrate digital threads-end-to-end data pipelines linking design, procurement,\nconstruction, and operations-using SLMs for natural language querying and automation.\nDevelop complex knowledge graphs to model intricate relationships in EPC ecosystems (e.g., asset\nhierarchies, supplier networks, regulatory compliance), enhanced by SLM-driven reasoning and\ngraph RAG.\nDeploy edge AI solutions for real-time applications, including IoT sensor fusion, on-device anomaly\ndetection, and federated learning in harsh manufacturing environments.\nPrototype and benchmark systems for scalability, collaborating on MLOps pipelines with tools like\nKubernetes, TensorRT, and ONNX for edge-cloud hybrid deployments.\nConduct research on SLM fine-tuning, quantization, and agentic behaviors, documenting findings for\ninternal adoption or publications.\nSensitivity: LNT Construction Internal Use\nRequired Qualifications:\nMaster's or PhD in AI/ML, Computer Science, or related field (or equivalent experience).\n4+ years in applied AI, with deep hands-on experience building/training SLMs or derived models\n(e.g., LoRA/PEFT fine-tuning, model distillation).\nProven expertise in edge AI deployment (e.g., NVIDIA Jetson, ARM-based inference, quantization\ntechniques like AWQ/GPTQ).\nStrong experience with agentic AI (e.g., LangChain, AutoGen) and Neo4j for complex knowledge\ngraphs (Cypher queries, graph embeddings, graph algorithms).\nFamiliarity with digital threads in manufacturing/EPC: PLM integration, BIM data flows, or IIoT\nprotocols.\nProficiency in PyTorch, Hugging Face Transformers, Neo4j, and edge frameworks (e.g., TensorFlow\nLite, OpenVINO).\nPreferred Skills:\nExperience in EPC/manufacturing domains: procurement-to-pay automation, digital twins, or\nsupply chain graph analytics.\nKnowledge of federated learning, secure multi-party computation, or quantum-safe edge crypto.\nContributions to open-source SLM/edge AI projects or relevant publications (e.g., ICML, NeurIPS\nworkshops).\nSensitivity: LNT Construction Internal 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