Staff Engineer I

responsibilities will include: Lead the experimentation, design and build of agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention. Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling. Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation. Support POCs/ Pilots projects to explore and validate innovative technologies and solutions in ML/AI space. Build and operate machine learning models on diverse, high-volume data sources for forecasting, classification and prediction. Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact. Design pipelines for data preparation, model training, evaluation, and deployment in collaboration with engineering counterparts. Define best practices for monitoring ML model performance using statistical techniques; identifying drifts, failure modes, and improvement opportunities. Contribute to ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences. Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams. Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities. Document designs, development processes, and best practices to promote knowledge sharing and operational efficiency. What do we expect from you? We are seeking candidates with deep expertise in ML and hands-on experience building Gen AI and Agentic AI systems. Skillset: AI/ML Engineering, Python, LLM, RAG, Agentic AI, MCP, REST API You should have experience with: Design, build, and deploy building LLM-powered applications, chatbots, and multi-agent systems to improve efficiency, accuracy, and decision-making. Implement and customize Gen AI and multi-agent systems using AI frameworks such as Lang Chain, Lang Graph, Crew AI, or similar. Create, refine, and maintain prompt templates, chains, and workflows. Integrate vector databases (Milvus DB, FAISS, Pinecone, Chroma, etc.) for semantic search and memory. Classical ML and deep learning techniques across NLP, clustering, and time series. Proficient in deploying ML workflows/models in production system. Continuous monitoring, evaluation, and retraining of models to ensure sustained performance. Ensuring model explainability, accuracy, compliance. Big data processing (Spark, distributed data systems) and cloud computing. Designing end-to-end ML solutions—from prototype to production. Stay updated with emerging AI/ML technologies and recommend relevant innovations for the finance domain. Drive in design reviews, sprint planning, and technical discussions. Collaborating effectively with diverse stakeholders, including technical teams and business leaders. Adapting to fast-paced environments and evolving priorities with high energy and autonomy. We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact. Qualifications Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. Ph D is a plus. 8+ years of industry experience in ML/Data Science, with deep proficiency in Python and one or more frameworks: Py Torch, Tensor Flow, Scikit-learn. Solid understanding of ML fundamentals, regression, tree-based models, clustering, and time series. Hands-on experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable. Experience building Agentic frameworks such as Lang Graph, Auto Gen, Crew AI, n8n or Re ACT-style agents. Expertise with algorithms in NLP, Time Series, and Deep Learning, applied on real-world datasets. Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI). Experience with Model Context Protocols (MCPs) — building or integrating MCP tools, servers, or capabilities. Comfortable working in cross-functional teams. Strong problem-solving and analytical skills with the ability to work in agile environments. Good understanding of data pipelines, APIs, and cloud platforms (Azure/GCP/AWS). Excellent communication skills with the ability to simplify complex technical concepts. The In Mobi Culture At In Mobi, culture isn’t a buzzword; it's an ethos woven by every In Mobian, reflecting our diverse backgrounds and experiences. We thrive on challenges and seize every opportunity for growth. Our core values — thinking big, being passionate, showing accountability, and taking ownership with freedom — guide us in every decision we make. We believe in nurturing and investing in your development through continuous learning and career progression with our In Mobi Live Your Potential program. In Mobi is proud to be an Equal Employment Opportunity employer and is committed to providing reasonable accommodations to qualified individuals with disabilities throughout the hiring process and in the workplace. Visit https://www.inmobi.com/company/careers to better understand our benefits, values, and more! 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