AI Engineer

Role: AI Engineer
[Immediate Joiners Only]

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 Git Hub  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  Lang Chain, Autogen, or Lang Graph .
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  Lang Graph, 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 (Num Py, Pandas, Scikit-Learn, FastAPI, Flask)
Databases:  SQL, MySQL, MongoDB
LLM / GenAI Frameworks:  Lang Chain, Autogen, Lang Graph, 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:

Delhi NCR / Bhopal
Reporting To:  Leader – AI Engineering
Cadence &

Collaboration:

Weekly team meetings; close collaboration with AI, Data, and Engineering teams.