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About Client Our client was founded by IIT Kanpur graduates (2005 batch) operates 3 verticals: AI & ML services - building artificial-intelligence solutions for clients in the US. Media Analytics - working with large YouTube channels and media houses. Talent Solutions - meeting staffing and technology-hiring needs for large enterprises and mid-size companies. The selected person is hired as a full-time employee (FTE) on our client's payroll and is deployed to work on the end client's projects. About the Engagement The role sits within the AI engineering team of a large global financial services enterprise, building production AI applications on top of large language models. The team's focus is retrieval-augmented systems that let business users query large internal document estates accurately and reliably, together with the services and APIs that put those capabilities into everyday use. The work is hands-on and product-facing, with early exposure to emerging agent-based architectures. About the Role You will design and build intelligent AI-driven applications using large language models, retrieval-augmented generation (RAG) pipelines and emerging agent-based systems. This is a hands-on engineering role - implementing scalable AI solutions end to end, contributing to architecture, and working closely with lead engineers, product managers and data scientists. It suits an engineer with genuine machine-learning and NLP foundations who has since moved into building and shipping LLM-based applications. Key Responsibilities AI/ML & NLP development - Design and implement ML/NLP models and pipelines; perform data preprocessing, feature engineering and model evaluation; contribute to improving model accuracy, robustness and efficiency. LLM & generative AI - Build applications using large language models; develop and optimise prompts, embeddings and inference workflows; support fine-tuning and evaluation of LLM-based systems. RAG pipelines - Develop and maintain retrieval-augmented generation pipelines integrating vector search with LLMs; work with embedding models and vector stores; improve retrieval quality and response relevance. Agent-based systems - Contribute to agent-based workflows and orchestration logic; implement basic agent coordination and tool integrations; work with emerging frameworks such as LangChain agents or Google ADK. System development & deployment - Build reusable, scalable AI services and APIs; develop solutions for real-time and batch inference; ensure code quality, performance optimisation and reliability. Collaboration & execution - Work closely with lead engineers, product managers and data scientists; participate in design discussions and technical reviews; contribute to documentation and knowledge sharing. Required Skills & Experience Experience - 5 7 years of software engineering experience, including 3 years in machine learning / NLP and 1.5+ years building LLM-based applications. RAG (Retrieval-Augmented Generation) - Hands-on experience building and improving retrieval pipelines: chunking, embeddings, retrieval quality and response relevance. Vector databases - Practical experience with any one of Elasticsearch, OpenSearch, FAISS or a similar vector store, including embedding generation and similarity search. Python - Strong, production-grade coding skills in Python. LLM application development - Hands-on experience building applications with any one of GPT, Llama, Gemini, Claude or a similar model, including prompt engineering and LLM evaluation. NLP fundamentals - Solid grounding in tokenization, embeddings, transformers and model evaluation. APIs & deployment - Experience with REST APIs / microservices, Docker, and any one cloud platform (Azure, AWS, GCP or OCP). Nice-to-Have / Preferred LLM frameworks - Exposure to any one of LangChain, Google ADK or a similar framework. Agent-based systems - Exposure to agent workflows, tool-based reasoning and orchestration patterns. ML frameworks - Working exposure to any one of PyTorch, TensorFlow or a similar framework. Model training & fine-tuning - Familiarity with model training and fine-tuning approaches. MLOps & monitoring - Exposure to MLOps tooling, model monitoring and drift detection; Kubernetes an advantage. Search & knowledge - Familiarity with hybrid search or knowledge graphs. Domain - Financial services / BFSI exposure advantageous. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.