{"id":1272532,"url":"https://alion.io/job/qubelabs-senior-aiml-engineer","title":"Senior AI/ML Engineer","company":{"id":3808921,"name":"QubeLabs","domain":"qubelabs.ai","url":"https://alion.io/company/qubelabs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Delhi, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GitHub","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NER","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SFT","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"Tool Use","optional":false},{"name":"Anthropic","optional":true},{"name":"AutoGen","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"CrewAI","optional":true},{"name":"Docker","optional":true},{"name":"DPO","optional":true},{"name":"FastAPI","optional":true},{"name":"GCP","optional":true},{"name":"Gemini","optional":true},{"name":"Gemma","optional":true},{"name":"Git","optional":true},{"name":"Hugging Face","optional":true},{"name":"Hybrid Search","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Linux","optional":true},{"name":"Llama","optional":true},{"name":"Mistral","optional":true},{"name":"MLFlow","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"OpenAI","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Qdrant","optional":true},{"name":"Qwen","optional":true},{"name":"Redis","optional":true},{"name":"Rest API","optional":true},{"name":"SGLang","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Transformers","optional":true},{"name":"vLLM","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-09-01T17:23:46Z","employer_posted_date":null,"last_verified_at":"2026-09-01T17:23:46Z","board_verified":false,"closed_at":null,"days_open":30,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":30},"description":"AI/ML Engineer - LLM & Enterprise AI Runtime\n\nLocation: New Delhi, India (On-site)\n\nEmployment Type: Full-time\n\nAbout QubeLabs\n\nQubeLabs is building the next generation of Enterprise AI Systems that transform workforce operations and intelligence for the financial services industry. Our platform combines conversational AI, agentic workflow automation, proprietary language models and enterprise intelligence to transform how financial services operate.\n\nOur product \"QubeLabs Workmate\" is purpose-built for banks, NBFCs, MFIs, wealth management firms, insurance companies and fintechs across India and Europe to improve enterprise efficiency, productivity and customer experiences at scale.\n\nWe're looking for an exceptional AI/ML Engineer who is passionate about building production-grade Enterprise AI systems and enjoys working at the intersection of Large Language Models, real-time AI runtimes, enterprise intelligence and AI orchestration.\n\nRole Overview\n\nAs an AI/ML Engineer, you will build and optimize the Enterprise AI Runtime powering QubeLabs Workmate. You will work closely with founders, product teams, Full Stack Engineers and Speech AI Engineers to develop real-time conversational AI, enterprise copilots and intelligent workflow automation.\n\nThis role is ideal for engineers with deep expertise in Large Language Models, AI orchestration, retrieval systems and low-latency inference. You will leverage modern AI engineering tools such as Cursor, Claude Code, GitHub Copilot and similar AI development assistants to accelerate research and development while maintaining production-grade AI systems. You will contribute to the core intelligence layer powering conversational AI, reasoning, tool calling, enterprise knowledge retrieval and agentic workflows.\n\nKey Responsibilities\n\nEnterprise AI Runtime\n\nDesign and develop the Enterprise AI Runtime for conversational AI, enterprise copilots and intelligent workflow automation.\nBuild AI orchestration capabilities including context management, memory, tool calling and model routing.\nDevelop low-latency inference pipelines for real-time voice conversations and AI copilots.\nDesign intelligent execution strategies for enterprise workflows and AI services.\nOptimize AI runtime for scalability, observability, reliability and production deployment.\n\nLLM Engineering\n\nFine-tune and optimize open-source Large Language Models and multimodal models using Supervised Fine-Tuning (SFT), instruction tuning and preference optimization techniques.\nBuild enterprise reasoning, structured output generation and domain-specific AI capabilities.\nDevelop Retrieval-Augmented Generation (RAG), semantic search and enterprise knowledge retrieval systems.\nImprove model accuracy through prompt engineering, grounding, hallucination reduction and evaluation frameworks.\nDevelop multilingual reasoning and enterprise domain adaptation capabilities.\n\nAI Platform Development\n\nDevelop model serving APIs, reusable inference services and AI SDKs.\nOptimize model serving using quantization, batching, caching and GPU acceleration.\nBuild AI evaluation pipelines, benchmarking frameworks and performance monitoring.\nCollaborate with Speech AI Engineers to integrate ASR, NLU, NER and TTS pipelines with LLM-powered conversational systems.\n\nEngineering Excellence\n\nBuild reproducible AI pipelines and MLOps workflows.\nParticipate in AI architecture, model evaluation and system design discussions.\nLeverage AI-assisted engineering tools to improve research and development productivity.\nFollow engineering best practices for experimentation, versioning, deployment, monitoring and continuous improvement.\n\nQualifications\n\nEducation\n\nBachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning or a related discipline.\nGraduates from Tier-1 institutions are preferred.\n\nExperience\n\n3-6 years of experience in AI/ML Engineering, Applied AI or Machine Learning.\nExperience building production-grade AI systems is preferred.\nExperience with Large Language Models, conversational AI, enterprise AI platforms or real-time AI systems is highly desirable.\nExperience in model fine-tuning, inference optimization, AI orchestration or Retrieval-Augmented Generation (RAG) will be an added advantage.\nCandidates should demonstrate strong proficiency in AI-assisted engineering and the ability to significantly improve research and development productivity using modern AI engineering tools while maintaining production-grade AI systems.\n\nRequired Skills\n\nLarge Language Models :Open-source LLMs (Llama, Qwen, Gemma, Mistral or equivalent), Multimodal Models, Hugging Face Transformers, vLLM, SGLang, TensorRT-LLM, Prompt Engineering, Structured Output Generation, Function Calling and Tool Calling.\n\nEnterprise AI Runtime:AI Orchestration, Multi-Agent Systems, Context Management, Session Memory, Conversation Runtime, Retrieval-Augmented Generation (RAG), Hybrid Search, Vector Databases (Qdrant, Pinecone or equivalent), Model Context Protocol (MCP), Agent Frameworks (LangGraph, AutoGen, CrewAI or equivalent).\n\nModel Engineering: Supervised Fine-Tuning (SFT), Instruction Tuning, Preference Optimization (DPO or equivalent), Model Evaluation, Benchmarking, Hallucination Reduction, Grounding, Quantization, GPU Optimization and Inference Optimization.\n\nProgramming: Python, PyTorch, FastAPI, Hugging Face, REST APIs, Docker, Redis, PostgreSQL, Git and Linux.\n\nCloud & MLOps: Docker, Kubernetes, AWS/Azure/GCP, MLflow, Weights & Biases, CI/CD, Model Deployment, Monitoring and Observability.\n\nAI Development\n\nWorking knowledge of:\n\nOpenAI, Anthropic, Google Gemini and Open Source AI Models\nAI SDKs and Inference Frameworks\nAI Evaluation Frameworks\nStreaming APIs\nEnterprise AI Architectures\nWorking knowledge of Speech AI pipelines (ASR, NLU, NER and TTS) and experience integrating speech AI with LLM-powered conversational systems will be an added advantage.\nMandatory: Hands-on proficiency using Cursor, Claude Code, GitHub Copilot or equivalent AI engineering assistants as part of day-to-day AI development.\n\nPersonal Attributes\n\nStrong analytical and problem-solving skills.\nPassion for AI research and applied engineering.\nCurious about emerging AI technologies.\nStrong software engineering mindset.\nDetail-oriented with excellent execution capability.\nSelf-driven with a high sense of ownership.\nExcellent communication and collaboration skills.\nAbility to thrive in a fast-paced startup environment.\n\nWhat Success Looks Like\n\nWithin the first year, you will:\n\nBuild the Enterprise AI Runtime powering QubeLabs Workmate.\nDeliver production-ready LLM capabilities for conversational AI and enterprise copilots.\nDevelop scalable AI orchestration, context management and retrieval systems.\nOptimize inference pipelines for low-latency, high-performance enterprise deployments.\nContribute significantly to the architecture and intelligence foundation of QubeLabs' next-generation Enterprise AI Platform.\n\nWhy Join QubeLabs?\n\nBuild the future of Enterprise AI Systems.\nWork directly with founders and leadership.\nDevelop next-generation AI systems for leading financial institutions.\nWork on cutting-edge technologies including Conversational AI, Agentic AI, Enterprise Intelligence, Real-Time AI Copilots and Sovereign AI.\nExposure to global markets across India and Europe.\nHigh ownership, rapid learning and accelerated career growth.\n\nPreferred Candidate Profile\n\nGraduate from Tier-1 engineering institutions (IITs, IIITs, IISc, IIIT Hyderabad, BITS Pilani, NITs, NSUT, DTU or equivalent).\nStrong GitHub, Hugging Face or open-source portfolio demonstrating production AI work.\nExperience building real-time conversational AI, enterprise copilots or AI-native platforms.\nExperience with low-latency inference, distributed model serving, AI orchestration, GPU optimization or multimodal AI systems is highly preferred.\nContributions to open-source AI projects, research publications or enterprise AI products will be an added advantage.\nPassion for AI, technology and building next-generation Enterprise AI Systems.\n\nQubeLabs is an equal opportunity employer committed to building a diverse and inclusive workplace where innovation thrives\nSkills\nMachine Learning, Artificial Intelligence, FastAPI, Conversational AI, 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