Data Scientist II

Akaike Technologies Pvt. Ltd.; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Akaike Technologies in Bengaluru seeks a hands-on Data Scientist II to design and deploy AI solutions across Generative AI, agentic systems, and traditional ML. You’ll own end-to-end projects from rapid prototyping to scalable production, working on real business problems in pharma or life sciences a strong plus.

You will build GenAI solutions, orchestrate multi-agent workflows, evaluate safety and accuracy, and apply forecasting, CLV, recommendation, and NLP techniques.

Qualifications

• 3–4 years building and deploying ML models in production.
• Generative AI with LLMs, RAG, and agentic frameworks such as LangChain.
• Hands-on with tool use, function calling, prompt engineering, and guardrails.
• Strong ML and deep learning fundamentals (ANN, CNNs, LSTMs, Transformers).
• Traditional NLP skills: Transformers, Word2Vec, NER, topic modeling.
• Expert Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow) and SQL; PySpark for large data.
• Solid system design and MLOps — Docker and AWS.

Responsibilities

• GenAI & Agents: Build and deploy GenAI solutions and agentic systems with RAG pipelines.
• Multi-Agent Orchestration: Design workflows using ReACT and planner–executor patterns.
• Reliability & Evaluation: Create evaluation frameworks for safety and accuracy of LLMs.
• End-to-End ML: Apply forecasting, CLV, recommendation, and NLP; ship production systems.
• Big Data: Process billions of records with PySpark to extract insights.
• Experimentation: Run experiments and present results to stakeholders.
• Mentorship: Guide junior team members across engineering and product teams.

Skills

Generative AI

Python ML

SQL & PySpark

MLOps AWS

NLP Transformers

Agentic systems

Prompt engineering

Tools

LangChain

LangGraph

Docker

AWS

Pinecone

Milvus

Weaviate

PySpark

TensorFlow

PyTorch

Job description

About the Role

Akaike Technologies builds AI-driven solutions across industries, using data and AI to drive growth, efficiency, and value for our clients. We’re hiring a hands‑on Data Scientist II to design and deploy solutions across Generative AI, agentic systems, deep learning, and classical ML. You’ll own systems end‑to‑end — from rapid prototyping to scalable production — working close to real business problems. Pharma or life‑sciences experience is a strong plus.

Key Responsibilities

• GenAI & Agents: Build and deploy Generative AI solutions — agentic systems and RAG pipelines — including agents that reason, use tools, and act over structured (databases, APIs) and unstructured data.
• Multi-Agent Orchestration: Design multi‑agent workflows using patterns like ReACT, planner–executor, and agent‑critique to break down and solve complex tasks.
• Reliability & Evaluation: Establish evaluation frameworks — LLM‑as‑a‑judge, groundedness/faithfulness, hallucination detection — to test and ensure the safety and accuracy of LLM systems.
• End‑to‑End ML: Apply forecasting, CLV, recommendation, and NLP techniques, owning the full lifecycle from prototype to scalable, low‑latency deployment (Docker, AWS).
• Big Data: Process billions of records with PySpark to engineer features and extract actionable insights.
• Experimentation: Run experiments and EDA to validate hypotheses, and communicate results and solution outlines clearly to stakeholders.
• Mentorship: Mentor junior team members and bridge business problems and data science across engineering and product teams.

Core Qualifications & Skills

• 3–4 years building and deploying machine-learning models in production.
• Generative AI with LLMs — RAG (chunking, embeddings, vector search with Pinecone/Milvus/Weaviate, re‑ranking) and agentic frameworks (LangChain, LangGraph).
• Hands‑on with tool use / function calling, prompt engineering, and structured outputs / guardrails.
• Strong classical ML and deep learning (ANN, CNNs, LSTMs, Transformers).
• Traditional NLP: Transformers (BERT, T5, GPT), Word2Vec, NER, topic modeling, contrastive learning.
• Expert Python (Pandas, NumPy, Scikit‑learn, PyTorch/TensorFlow) and strong SQL; PySpark for distributed processing.
• Solid system design and MLOps — Docker and AWS (S3, Lambda, ECR, Step Functions).

Preferred Qualifications

• Pharma or life‑sciences domain, with claims data and commercial analytics.
• Fine‑tuning open‑source LLMs (Llama, Mistral) for specific tasks.
• Latest agentic systems — MCP and multi‑agent frameworks (AutoGen, CrewAI).
• Advanced ML: PU learning, representation learning, and model explainability.
• Domain models such as Marketing Mix Models (MMM), demand forecasting, or multi‑dimensional time‑series.
• Open‑source AI/ML contributions or publications.
• Competitive ESOP grants.
• Working with Fortune 500 companies and world‑class teams.
• Publishing papers and attending conferences.
• Networking events, conferences, and seminars.
• Visibility across all functions at Akaike — sales, pre‑sales, lead generation, marketing, and hiring.

Required Skills

Data Science Machine Learning Agentic AI generative AI