AI Engineer

Role: AI EngineerAbout Sutra.AI Sutra.AI is a rapidly growingAI Enterprise SaaS Platformcompany focused on buildingdata-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 anAI Engineerwho is passionate about buildingreal-world, production-ready AI systemsusingmachine 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 transformscomplex business problemsintodeployable AI systemsthat deliver measurable value. Everymodel, LLM workflow, and intelligent agentyou build directly enhances the Sutra.AI platform-drivingautomation, scalability, and decision intelligencefor customers worldwide. This role ensures that innovation moves beyond experimentation to becomeproduction-grade capabilitiesthat define the Sutra.AI experience.Must-Have Qualifications Bachelor’s or Master’s degree inComputer Science, Data Science, AI/ML, or related disciplines . 3–4 yearsof hands-on experience inAI/ML, LLMs, or Generative AIapplication development. Experience withRAG (Retrieval-Augmented Generation)andAgentic Frameworks . Prior experiencefine-tuning models(OpenAI, LLaMA, Mistral, Falcon, etc.) preferred. Portfolio or GitHubshowcasing AI or GenAI projects.Key Responsibilities 1. AI/ML Model Development Build and optimize supervised and unsupervised ML models usingPython and Scikit-Learn . Performfeature engineering, data wrangling, and model evaluationfor structured and unstructured datasets. Applyevaluation metrics(ROC-AUC, F1, RMSE, Precision, Recall) for benchmarking. Collaborate with Data and Engineering teams to ensurereproducibility and deployment readiness . 2. Generative AI & LLM Engineering Develop and integrateLLM-based applicationsusingLangChain, Autogen, or LangGraph . Performfine-tuning and instruction-tuningusingHugging Face Transformers, PEFT, LoRA, or OpenAI APIs . Optimizeprompts, model parameters, and responsesfor factual accuracy and contextual relevance. ImplementRAG pipelinesusingPinecone, FAISS, Chroma, or Weaviate . Buildevaluation pipelinesto assess LLM output quality, coherence, and bias. 3. AI Agent Development Design and deployautonomous AI agentscapable of reasoning, planning, and multi-step tool use. Leverage frameworks such asLangGraph, Autogen, or CrewAIformulti-agent systems . Integrate agents withAPIs, databases, and internal platformsto automate workflows. Enhancereliability, scalability, and maintainabilityof deployed AI systems. 4. Continuous Improvement & Documentation Maintaincomprehensive documentationand model tracking for reproducibility. Collaborate with cross-functional teams forsmooth integrationinto customer solutions. Participate inpeer reviews and sprint retrospectivesto ensure quality and delivery efficiency. Research and adoptemerging AI/ML and agentic advancementsto 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 toolsSoft Skills Deep curiosity and passion for emerging AI technologies. Clear, structured communication and documentation ability. Ability totranslate technical outcomesfor 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:Remote / Bhopal / Noida Reporting To:Leader – AI Engineering Cadence & Collaboration:Weekly team meetings; close collaboration with AI, Data, and Engineering teams.

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