Ai engineer

Role: AI Engineer About Sutra. AI Sutra. AI is a rapidly growing AI Enterprise Saa S 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 (Open AI, LLa MA, Mistral, Falcon, etc.) preferred. Portfolio or Git Hub showcasing AI or Gen AI 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, Lo RA, or Open AI 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 Crew AI 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, Fast API, Flask) Databases: SQL, My SQL, Mongo DB LLM / Gen AI Frameworks: Lang Chain, Autogen, Lang Graph, Hugging Face Transformers Fine-Tuning Techniques: Instruction-Tuning, PEFT, Lo RA, Adapter Training, RLHF Evaluation & Optimization: BLEU, ROUGE, BERTScore, factuality checks, toxicity filtering Vector Databases: Pinecone, FAISS, Chroma, Weaviate Bonus Tools: Streamlit, Gradio, Open AI/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: Remote / Bhopal / Noida Reporting To: Leader – AI Engineering Cadence & Collaboration: Weekly team meetings; close collaboration with AI, Data, and Engineering teams.