Senior Data Scientist

Senior Data Scientist Experience: 4+ Years | Location: Bengaluru (Hybrid) |Team: Data Science & AI About the Role Akaike Technologies builds agentic AI and machine-learning systems that power decision-making for global enterprises. We want a Senior Data Scientist who understands what makes agentic systems reliable and can architect complex, high-accuracy, human-in-the-loop AI for demanding clients. Roughly 70% Generative AI and agents, 30% classical ML and large-scale data, with room to move across problems as priorities shift. Key Responsibilities Generative AI & Agentic Systems (primary focus) Agentic Systems: Design and ship production multi-agent systems (LangGraph/CrewAI; ReACT, Agent-Critique) that generate reliable, structured, high-accuracy outputs. Grounding & Reliability: Engineer grounding, source traceability, and hallucination control so outputs hold up in production and regulated settings. Evaluation: Build quantitative evaluation for generation quality — faithfulness, hallucination, consistency, and LLM-as-a-judge frameworks. Human-in-the-Loop: Design configurable, human-in-the-loop workflows (review gates, co-pilot editing) that scale across clients and use cases. Retrieval & Tuning: Build production RAG and agentic retrieval; apply PEFT/LoRA on open-source models (Llama 3, Mistral) where it beats prompting. Classical ML & Data at Scale (secondary focus) Custom Modeling: Build bespoke models for targeting, budget optimization, and churn, including PU and single-class learning on sparse, noisy data. Applied Deep Learning: Apply 1D/2D CNNs, LSTMs, and embeddings to non-text data (time-series, behavioral logs) where they add real signal. Big Data: Write optimized PySpark/SparkSQL on Databricks over billions of rows, with feature stores consistent across training and inference. Architecture, MLOps & Delivery System Design: Architect end-to-end systems with deliberate latency/cost/accuracy trade-offs and modular, reusable components. Measurement: Bring statistical rigor — offline and human eval, A/B where it fits, drift detection, and automated retraining. Deployment: Ship scalable pipelines on AWS (Bedrock, Lambda, Step Functions) and FastAPI. Leadership & Stakeholders Mentorship & Clients: Mentor juniors, run rigorous code reviews, and serve as the technical point of contact for clients — explaining model limitations and risk without overselling. Must-Have Skills Advanced GenAI/agent frameworks (LangChain, LlamaIndex, LangGraph, DSPy), with at least one agentic system or complex RAG pipeline shipped to production. PyTorch or TensorFlow; solid grasp of attention, encoder-decoder architectures, and embeddings. Expert PySpark and SQL; comfortable debugging and optimizing Spark on Databricks. Python (OOP, typing, rigorous standards) and a strong experimental / statistical mindset. Clear communicator who can explain complex AI — and its risks — to business leaders. Nice to Have Pharma,…