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Mid-level GenAI role, metro location, broad skillset and recognizable brand increases applicant competition substantially.
Core ML/AI skills transfer across industries, though knowledge-graph and CX integrations require domain familiarity.
Explicit years, mandatory deep-learning/GenAI experience, and extensive tech stack make screening relatively strict.
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Build and deploy advanced Generative AI applications including multimodal and agentic systems using LLMs and frameworks like LangChain, focusing on reasoning, planning, and knowledge graph integration.
Transform AI models into scalable APIs and microservices, deploy and monitor them in cloud environments (AWS/Azure/GCP) optimizing for cost, latency, and reliability.
Collaborate cross-functionally with engineering, data science, and CX teams to create personalized, knowledge-grounded AI solutions and mentor junior engineers.
3-4 years of software development experience with machine learning model deployment in production.
2+ years experience specifically with deep learning and Generative AI or transformer-based architectures.
Strong programming skills in Python and SQL; experience with API development and backend frameworks like Flask, FastAPI, or Django.
Hands-on experience with cloud platforms (AWS/Azure/GCP) for deploying and scaling AI systems, and big data technologies such as Apache Spark, Hadoop, and MongoDB.
Experienced in building complex GenAI applications beyond simple retrieval-augmented generation (RAG), including agentic frameworks, multimodal models, and custom fine-tuning of LLMs.
Comfortable working in a hybrid work environment in Pune (minimum 2 days onsite) with cross-team collaboration involving CX, engineering, and product stakeholders.
Operationally oriented with a proven track record of transforming AI research into production-grade scalable solutions and mentoring junior team members.