Applied AI Scientist, GenAI and ML Prototyping

C2FO; Dadri; On-site; INR 6,704,980 - 8,620,689; Full time

Benefits offered by this job

Comprehensive benefits package

Flexible work options

Volunteer time off

Job summary

C2FO is searching for an Applied AI Engineer in Dadri, India. The ideal candidate will engage in prototyping AI solutions across operations and customer-facing products. Responsibilities include leading discovery sessions, rapid prototyping, and stakeholder management. Required qualifications include over four years in data science and proficiency in Python, SQL, and familiarity with LLMs. A Bachelor's in a relevant field is essential. Competitive benefits and a commitment to diversity and inclusion are offered.

Qualifications

• 4+ years in data science, machine learning, or related field.
• 2+ years with large language models or Generative AI solutions.
• Experience with non-technical stakeholders.

Responsibilities

• Lead identification of AI solutions across operations.
• Build functional prototypes with appropriate approaches.
• Define success criteria and present findings to sponsors.

Skills

Proficiency in Python

SQL skills

Experience with LLM APIs

Familiarity with deep learning frameworks

Education

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering

Tools

Git

Jupyter

Docker

Job description

We are seeking an Applied AI Engineer to lead identification and rapid prototyping of AI solutions across C2FO’s operations and customer‑facing products. The role focuses on discovery, proof of concept, and handoff to production.

Core Responsibilities

• Run structured discovery sessions with department heads and product owners to identify and scope AI opportunities. Define clear problem statements, including data availability and constraints.
• Rapid prototyping: build functional prototypes using the most appropriate approach (RAG pipelines, agentic workflows, predictive ML models, or rule‑based systems).
• Stakeholder management: serve as primary technical contact, communicate trade‑offs around accuracy, cost, and latency, and recommend against building when evidence suggests it.
• Evaluation & validation: define success criteria before building, design and run evaluations, present findings to non‑technical sponsors.
• Technical handoff: produce documentation covering system design, prompt strategies, data requirements, known failure modes, and evaluation benchmarks.

Tech Stack & Technical Requirements

Core Languages & Frameworks

• Proficiency in Python (Pandas, NumPy, Scikit‑learn)
• SQL skills for querying modern data warehouses (BigQuery, Snowflake, PostgreSQL)
• Working knowledge of deep learning frameworks (PyTorch, TensorFlow) is nice to have.

LLM & Generative AI Tooling

• Hands‑on experience with LLM APIs (OpenAI, Anthropic, Google)
• Command of prompt engineering techniques (few‑shot, chain‑of‑thought, structured output)
• Experience with open‑source LLMs (Mistral, LLaMA) and understanding of open vs proprietary models.

Agentic Orchestration & RAG

• Experience building RAG pipelines, chunking strategies, embedding models, retrieval tuning.
• Familiarity with agentic orchestration frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen).
• Experience integrating vector databases (pgvector, Pinecone, Weaviate, ChromaDB).
• Understanding of tool/function calling patterns for LLM‑driven automation.

Evaluation & Experimentation

• Define and implement “good enough” metrics and evaluation frameworks.
• Experience with LLM evaluation libraries (RAGAS, TruLens, DeepEval).
• Skilled with experiment tracking tools (MLflow, Weights & Biases).
• Comfort with cost and latency profiling to inform feasibility decisions.

Data & Infrastructure

• Comfortable working within cloud environments (AWS, GCP, Azure).
• Ability to integrate with REST APIs and third‑party data sources.
• Proficiency with development tools: Git, Jupyter, VS Code.
• Basic familiarity with Docker for packaging POCs.

Qualifications

Required Experience

• 4+ years in data science, machine learning, or related field with end‑to‑end project delivery.
• 2+ years with large language models or Generative AI solutions professionally.
• Track record of taking projects from business discovery to working prototype.
• Direct engagement with non‑technical stakeholders, setting expectations, and communicating results.
• Strong background in traditional ML (classification, regression, clustering, NLP) plus modern LLM methods.

Education

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative field.
• Master’s or PhD is a plus but equivalent experience is valued.

Soft Skills & Ways Of Working

• Translate technical outputs into clear business value; comfortable in boardroom and notebook.
• Strong stakeholder management and realistic expectation setting around LLM capabilities, limits, cost.
• Excellent written communication for documenting prompt strategies, data requirements, POC logic.
• Self‑directed with tolerance for ambiguity; energised by open‑ended discovery.
• Structured thinker who defines success criteria before building.

Nice to Have

• Fine‑tune or instruction‑tune LLMs on domain‑specific datasets.
• Familiarity with responsible AI (bias detection, fairness, transparency).
• Prior consulting, pre‑sales engineering, or business‑facing technical role experience.
• Knowledge of business process mapping (BPMN) to support structured discovery sessions.

Benefits

We offer a comprehensive benefits package, flexible work options, volunteer time off, and more.

Commitment to Diversity and Inclusion

We are an Equal Opportunity Employer. We do not discriminate based on race, religion, color, sex, gender identity, sexual orientation, age, disability, veteran status, or any legally protected category. All hiring decisions are based on qualifications, merit, and business needs.