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 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:

Delhi NCR / Bhopal Reporting To:

Leader – AI Engineering Cadence & Collaboration:

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