Lead AI Engineer

Established in 2019, Credit Saison India (CS India) is one of the country’s fastest growing Non-Bank Financial Company (NBFC) lenders, with verticals in wholesale, direct lending and tech-enabled partnerships with Non-Bank Financial Companies (NBFCs) and fintechs. Its tech-enabled model coupled with underwriting capability facilitates lending at scale, meeting India’s huge gap for credit, especially with underserved and under penetrated segments of the population.Credit Saison India is committed to growing as a lender and evolving its offerings in India for the long-term for MSMEs, households, individuals and more. Credit Saison India is registered with the Reserve Bank of India (RBI) and has an AAA rating from CRISIL (a subsidiary of S&P Global) and CARE Ratings.Currently, Credit Saison India has a branch network of 80+ physical offices, 2.06 million active loans, an AUM of over US$2B and an employee base of about 1,400 employees.Credit Saison India is part of Saison International, a global financial company with a mission to bring people, partners and technology together, creating resilient and innovative financial solutions for positive impact. Across its business arms of lending and corporate venture capital, Saison International is committed to being a transformative partner in creating opportunities and enabling the dreams of people.Saison International is the international headquarters (IHQ) of Credit Saison Company Limited, founded in 1951 and one of Japan’s largest lending conglomerates with over 70 years of history and listed on the Tokyo Stock Exchange. The Company has evolved from a credit-card issuer to a diversified financial services provider across payments, leasing, finance, real estate and entertainment. Based in Singapore, Saison International’s global operations span over Singapore, India, Indonesia, Thailand, Vietnam, Mexico, Brazil, with active investments in debt, equity, corporate venture capital, and technology.

Partner with business, product, and engineering stakeholders to design and implement enterprise-scale AI solutions, with a strong emphasis on Generative AI applications (LLMs, multimodal, agentic AI).

Define and own the AI/ML roadmap for key problem areas, balancing near-term delivery with long-term innovation.

Lead design, prototyping, and deployment of Generative AI models (GPT, Claude, LLaMA, Mistral, Stable Diffusion) for production use cases.

Build and optimize data pipelines, retrieval-augmented generation (RAG) systems, embedding strategies, and integrations with vector databases (FAISS, Pinecone, Weaviate, Milvus).

Ensure robust model training, fine-tuning (LoRA, PEFT), orchestration (LangChain, LlamaIndex), monitoring, and governance.

Lead debugging and optimization of AI systems for latency, throughput, cost, and model drift/bias.

Collaborate with ML engineers, data scientists, and MLOps teams to design scalable deployment pipelines using modern cloud and containerized environments.

Mentor and guide engineers, setting best practices for experimentation, evaluation, and production readiness.

Keep abreast of latest AI/ML research in LLMs, CV, NLP, and multimodal learning, driving adoption of cutting-edge methods.

Translate complex AI concepts into business outcomes for non-technical stakeholders.

5–10 years of experience in AI/ML engineering, with at least 3+ years delivering Generative AI models into production.

Bachelor’s/Master’s/PhD in Computer Science, Mathematics, Statistics, or related field from a top-tier institution IITs/NITs/BITs etc.

Strong applied programming skills in Python, SQL, R and experience with data science libraries such as NumPy, Pandas, MatLab, scikit-learn.

Proven experience with deep learning frameworks: PyTorch, TensorFlow, Keras, MXNet, Caffe.

Familiarity with NLP and ML libraries: Transformers, SparkNLP, Gensim, SpaCy, NLTK, Hugging Face.

Experience building and fine-tuning LLMs and integrating them with orchestration frameworks (LangChain, LlamaIndex).

Expertise with vector databases (Pinecone, FAISS, Weaviate, Milvus) and knowledge of embedding retrieval patterns.

Cloud-native ML experience (AWS Sagemaker, GCP Vertex AI, Azure ML) and containerization (Docker, Kubernetes).

Applied knowledge of classical ML algorithms (SVM, Decision Trees, Random Forests, regression, clustering) alongside modern DL/GenAI approaches.

Strong knowledge of CI/CD for ML, model observability (MLflow, Weights & Biases, LangSmith), and governance frameworks.

Excellent problem-solving skills, communication, and ability to lead technical teams.