3 Agentops Jobs

Senior AI Engineer ???? Mumbai | Hybrid About Quantanite Were a global CX and digital solutions partner that blends cutting-edge AI with the human touch. Headquartered in London and operating across 4 continents, our 2,000+ people help some of the worlds fastest-growing brands scale smarter, work faster, and deliver better serviceevery time. Were not your typical outsourcing company. At Quantanite, great service is built on two things: smart tech and smarter people. From proprietary AI platforms like MBIUS to our collaborative, people-first culture, we equip our teams with the freedom and tools to build game-changing solutions. If youre looking for a place where your AI engineering skills can shape real enterprise impact (not just POCs), youll feel at home here. The Role As a Senior AI Engineer , youll own the full lifecycle of AI solutionsfrom design to production. Youll be hands-on with multi-agent workflows, advanced knowledge retrieval, and LLM optimization to solve enterprise-scale problems. Expect to lead projects such as AI Copilots, Enterprise Search Systems, Summarization Engines, and Intelligent Automation Tools building robust, scalable solutions that move beyond prototypes into production. Youll also play a key role in mentoring junior engineers, setting engineering standards, and collaborating cross-functionally to ensure delivery at scale. What Youll Do Build and deploy agentic systems and multi-agent workflows using LangChain, LlamaIndex, OpenAI SDK (and beyond). Design RAG pipelines with vector DBs like FAISS, Pinecone, Weaviate, or Qdrant. Fine-tune and optimize LLMs (GPT-4, Claude, LLaMA, Mistral, Gemini) with LoRA/QLoRA, quantization, and other techniques. Implement guardrails, safe API usage, and PII protection for secure deployments. Monitor/evaluate agent performance with LangSmith, AgentOps , and similar tools. Ship real-world enterprise AI use casesCopilots, orchestration engines, intelligent search, and more. Mentor and review the work of junior AI engineers, spreading best practices. Work with product, data, and DevOps teams to bring AI solutions into production at scale. What Youll Bring Solid grounding in LLMs and GenAI (fine-tuning, prompt engineering, practical deployment). Proven track record with RAG pipelines and vector databases. Hands-on with agent frameworks (LangChain, LlamaIndex, etc.) and orchestration. Experience deploying/scaling models on clouds (AWS, Azure, HuggingFace). Strong coding ability in Python or similar. Familiarity with LLM Ops : monitoring, evaluation, cost optimisation. Delivered enterprise-grade AI solutions (not just demos). Experience mentoring engineers and building technical excellence in a team. Bonus: contributed to open-source LLM projects or worked on autonomous agent use cases (research copilots, orchestration systems). Qualifications B.Tech (required). 4.57 years of relevant experience. Strong knowledge of GenAI, agentic systems, and multi-agent architectures. Attributes: clear communicator, strong leader, highly tech-savvy. Preferred: exposure to BPO/KPO environments or systems experience in enterprise AI/agentic tools. Why Quantanite ???? Hybrid work model. ???? Ongoing training, mentorship & career growth. ???? Inclusive, people-first culture. ???? Health insurance & provident fund. Quantanite is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive environment for all. ???? Ready to push agentic AI beyond theory into enterprise-scale impact Lets build the future together. Apply today to discover how we can build better, together. Show more Show less

You will be responsible for building curated enterprise-grade solutions for GenAI application deployment at a production scale for clients. This role demands a solid understanding and hands-on skills in GenAI application deployment, encompassing development and engineering skills. You will need to possess expertise in data ingestion, selecting the appropriate LLMs, implementing simple and advanced RAG, guardrails, prompt engineering for optimization, traceability, security, LLM evaluation, observability, and deployment at scale on cloud or on-premise. It is essential for candidates to demonstrate knowledge of agentic AI frameworks due to the rapid evolution of this space. Strong background in ML with engineering skills is highly preferred for the LLMOps role. You should have 3 - 5 years of experience working on ML projects, involving business requirement gathering, model development, training, deployment at scale, and monitoring model performance for production use cases. Proficiency in Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, AgentOps (optional) is crucial. Experience with proprietary and open-source large language models, LLM fine-tuning, creating distilled models from hosted LLMs, and building data pipelines for model training is required. You should also have experience in model performance tuning, RAG, guardrails, prompt engineering, evaluation, and observability. Prior experience in GenAI application deployment on cloud and on-premises at scale for production, creating CI/CD pipelines, working with Kubernetes, and deploying AI services on at least one cloud platform such as AWS, GCP, or Azure is necessary. Proficiency in creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen, and light-weight UI development using streamlit or chainlit (optional) is beneficial. Desired experience with open-source tools for ML development, deployment, observability, and integration is an added advantage. A background in DevOps and MLOps will be a plus. You should be familiar with collaborative code versioning tools like GitHub/GitLab and possess excellent communication and presentation skills. A degree in Computer Science, related technical field, or equivalent is required. If you are someone who thrives in a dynamic environment and enjoys collaborating with enthusiastic individuals, this opportunity is perfect for you.,

You will be responsible for building curated enterprise-grade solutions for GenAI application deployment at a production scale for clients. Your role will involve a solid understanding and hands-on skills for GenAI application deployment, which includes development and engineering tasks. This will include data ingestion, selecting suitable LLMs, implementing simple and advanced RAG, setting up guardrails, prompt engineering for optimization, ensuring traceability and security, evaluating LLMs, enabling observability, and deploying at scale on the cloud or on-premise. It is crucial that candidates also showcase knowledge on agentic AI frameworks, with a preference for those having a strong background in ML with engineering skills for the LLMOps role. The ideal candidate should possess 3 - 5 years of experience in working on ML projects, encompassing tasks such as business requirement gathering, model development, training, deployment at scale, and monitoring model performance for production use cases. Proficiency in Python, NLP, Data Engineering, Langchain, Langtrace, Langfuse, RAGAS, and optionally AgentOps is essential. Prior experience working with both proprietary and open-source large language models, fine-tuning LLMs, creating distilled models from hosted LLMs, building data pipelines for model training, and tuning model performance, RAG, guardrails, prompt engineering, evaluation, and observability is required. Moreover, candidates should have experience in GenAI application deployment on cloud and on-premises at scale for production, creating CI/CD pipelines, working with Kubernetes, deploying AI services on at least one cloud platform (AWS/GCP/Azure), creating workable prototypes using Agentic AI frameworks like CrewAI, Taskweaver, AutoGen, and optionally developing lightweight UI using streamlit or chainlit. Desired experience with open-source tools for ML development, deployment, observability, and integration, as well as a background in DevOps and MLOps, will be advantageous. Proficiency in collaborative code versioning tools such as GitHub/GitLab, along with strong communication and presentation skills, is essential. A B.E/B.Tech/M.Tech in Computer Science or a related technical degree or equivalent qualification is required. If you are someone who enjoys challenging growth opportunities and thrives in a dynamic environment working alongside enthusiastic over-achievers, this role might be the perfect fit for you.,