AI Engineer

Role: AI Engineer
About Sutra.AI
Sutra.AI is a rapidly growing AI Enterprise SaaS Platform company focused on building data-to-decision automation at scale.
Our mission is to help enterprises transform raw data into intelligent, actionable insights through AI, automation, and decision intelligence.
Role Summary
We’re seeking an AI Engineer who is passionate about building real-world, production-ready AI systems using machine learning, generative AI, and agentic frameworks.
The ideal candidate is hands-on, detail-oriented, and thrives in a fast-paced environment where ideas move quickly from prototype to production.
You will collaborate closely with the AI Productization, Data, and Engineering teams to design, develop, and optimize intelligent systems that power Sutra’s next-generation AI capabilities.
Why This Role Matters
AI lies at the heart of Sutra.AI’s mission.
The AI Engineer transforms complex business problems into deployable AI systems that deliver measurable value.
Every model, LLM workflow, and intelligent agent you build directly enhances the Sutra.AI platform-driving automation, scalability, and decision intelligence for customers worldwide.
This role ensures that innovation moves beyond experimentation to become production-grade capabilities that define the Sutra.AI experience.
Must-Have Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related disciplines.
3–4 years of hands-on experience in AI/ML, LLMs, or Generative AI application development.
Experience with RAG (Retrieval-Augmented Generation) and Agentic Frameworks.
Prior experience fine-tuning models (OpenAI, LLaMA, Mistral, Falcon, etc.) preferred.
Portfolio or GitHub showcasing AI or GenAI projects.
Key Responsibilities
1. AI/ML Model Development
Build and optimize supervised and unsupervised ML models using Python and Scikit-Learn.
Perform feature engineering, data wrangling, and model evaluation for structured and unstructured datasets.
Apply evaluation metrics (ROC-AUC, F1, RMSE, Precision, Recall) for benchmarking.
Collaborate with Data and Engineering teams to ensure reproducibility and deployment readiness.
2. Generative AI & LLM Engineering
Develop and integrate LLM-based applications using LangChain, Autogen, or LangGraph.
Perform fine-tuning and instruction-tuning using Hugging Face Transformers, PEFT, LoRA, or OpenAI APIs.
Optimize prompts, model parameters, and responses for factual accuracy and contextual relevance.
Implement RAG pipelines using Pinecone, FAISS, Chroma, or Weaviate.
Build evaluation pipelines to assess LLM output quality, coherence, and bias.
3. AI Agent Development
Design and deploy autonomous AI agents capable of reasoning, planning, and multi-step tool use.
Leverage frameworks such as LangGraph, Autogen, or CrewAI for multi-agent systems.
Integrate agents with APIs, databases, and internal platforms to automate workflows.
Enhance reliability, scalability, and maintainability of deployed AI systems.
4. Continuous Improvement & Documentation
Maintain comprehensive documentation and model tracking for reproducibility.
Collaborate with cross-functional teams for smooth integration into customer solutions.
Participate in peer reviews and sprint retrospectives to ensure quality and delivery efficiency.
Research and adopt emerging AI/ML and agentic advancements to strengthen Sutra’s AI stack.
Core Technical Competencies
Programming: Python (NumPy, Pandas, Scikit-Learn, FastAPI, Flask)
Databases: SQL, MySQL, MongoDB
LLM / GenAI Frameworks: LangChain, Autogen, LangGraph, Hugging Face Transformers
Fine-Tuning Techniques: Instruction-Tuning, PEFT, LoRA, Adapter Training, RLHF
Evaluation & Optimization: BLEU, ROUGE, BERTScore, factuality checks, toxicity filtering
Vector Databases: Pinecone, FAISS, Chroma, Weaviate
Bonus Tools: Streamlit, Gradio, OpenAI/Anthropic APIs, Prompt Optimization tools
Soft Skills
Deep curiosity and passion for emerging AI technologies.
Clear, structured communication and documentation ability.
Ability to translate technical outcomes for non-technical audiences.
Strong ownership, accountability, and commitment to delivery timelines.
Collaborative mindset and comfort in agile, cross-functional teams.
Success Metrics
Model Accuracy & Quality: Achieves or exceeds benchmark accuracy and consistency.
LLM Fine-Tuning Impact: Demonstrated improvement in model performance post-tuning.
Delivery Timeliness: On-time completion of key milestones and deliverables.
Documentation Completeness: Clear, reproducible code and experiment logs.
Role Logistics
Location: Should be based out of Delhi NCR / Bhopal
Reporting To: Leader - AI Engineering
Cadence & Collaboration: Weekly team meetings; close collaboration with AI, Data, and Engineering teams.

Work arrangement
Yes