Akaike Technologies
Website:
akaike.ai
Job details:
Data Scientist II
Experience: 3–4 Years| Location: Bengaluru (Hybrid) | Team: Data Science & AI
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.
Benefits & Perks
- 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.
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