Data Scientist II

Akaike Technologies Pvt. Ltd.; Bengaluru; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

Akaike Technologies Pvt. Ltd. seeks a hands-on Data Scientist II to design and deploy AI-driven solutions across Generative AI, agentic systems, and classic ML.

You will own end-to-end projects from rapid prototyping to scalable production, with pharma/Life sciences experience considered a strong plus. Responsibilities include building GenAI solutions, orchestrating multi-agent workflows, establishing evaluation frameworks, and delivering low-latency deployments using Docker and AWS.

Qualifications

• 3–4 years building and deploying ML models in production.
• Experience with GenAI, LLMs, RAG pipelines, and agentic frameworks.
• Hands-on with tool use, function calling, prompt engineering, and structured outputs/guardrails.

Responsibilities

• GenAI & Agents: Build and deploy Generative AI solutions — agentic systems and RAG pipelines.
• Multi-Agent Orchestration: Design multi-agent workflows using ReACT, planner–executor, and agent-critique patterns.
• Reliability & Evaluation: Establish evaluation frameworks — LLM-as-a-judge, groundedness/faithfulness, hallucination detection.
• End-to-End ML: Apply forecasting, CLV, recommendation, and NLP techniques from prototype to scalable deployment (Docker, AWS).
• Big Data: Process billions of records with PySpark to engineer features and insights.
• Experimentation: Run experiments and EDA; communicate results to stakeholders.
• Mentorship: Guide junior team members across engineering and product teams.

Skills

ML model deployment

GenAI with LLMs

Prompt engineering

Classical ML & DL

NLP & Transformers

Python

SQL

PySpark

MLOps

Tools

Docker

AWS

LangChain

Pinecone

Milvus

Weaviate

LangGraph

PySpark

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.