Manager - AI Delivery Product
Firstsource Solutions LimitedMatch Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessSenior, niche LLM and enterprise AI skillset in Hyderabad with moderate brand presence yields medium competition.
Role requires deep AI/LLM, MLOps, and enterprise platform experience, producing high background sensitivity.
Multiple mandatory skills (LLMs, LangChain, vector DBs, cloud, full-stack) plus 8+ years causes high strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, test, and deploy scalable full-stack AI and ML-powered enterprise applications with Python and modern front-end frameworks.
Lead technical design, development, code reviews, CI/CD, DevOps, and MLOps processes to maintain high-quality, secure, and scalable software architectures on AWS and/or Azure.
Own and evolve specific modules/features within Firstsource's AI product suite while mentoring engineers and collaborating with Product Management to align on technical backlog and releases.
Minimum Requirements
8-12 years of overall experience.
Strong mandatory programming expertise in Python and hands-on experience with Machine Learning frameworks (Scikit-learn, TensorFlow, PyTorch, XGBoost).
Mandatory experience with AWS and/or Azure cloud platforms; strong skills in full-stack development including backend (FastAPI, Flask, Django) and frontend (React.js, Angular, JavaScript, TypeScript).
Bachelor's or Master's degree in Computer Science or Engineering discipline.
Ideal Candidate Profile
Experienced technical leader actively involved in hands-on coding and architectural decision-making within AI/ML enterprise product development.
Proficient in emerging AI technologies including Generative AI, LLMs, RAG, AI Agents with knowledge of AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, and experience with Vector Databases.
Comfortable driving scalable, secure, and maintainable cloud-native applications with CI/CD pipelines, containerization (Docker, Kubernetes), and DevOps/MLOps practices in Agile environments.