AI Engineer
FetchJobs.co · India
- Hybrid
- Full-time
- $120,000 / year
- India
Job highlights
- Develop production-ready AI systems using ML, GenAI.
- Integrate LLM applications with frameworks like LangChain.
- Build and deploy autonomous AI agents.
- Collaborate with cross-functional teams on AI solutions.
- Enhance enterprise data with AI-driven insights.
About the role
About the Company
Sutra.AI is a rapidly growing AI Enterprise SaaS Platform company dedicated to transforming raw data into intelligent, actionable insights through advanced AI, automation, and decision intelligence solutions. Our mission is to empower enterprises to harness the full potential of their data by building scalable, reliable, and innovative AI-driven systems that streamline decision-making processes. With a focus on delivering enterprise-grade AI applications, Sutra.AI continuously pushes the boundaries of technology to provide cutting-edge solutions tailored to complex business challenges. Our team comprises passionate professionals committed to excellence, innovation, and creating value for our global clientele.
About The Role
We are seeking a talented and dedicated AI Engineer to join our dynamic team at Sutra.AI. The ideal candidate will have a deep passion for developing real-world, production-ready AI systems utilizing the latest machine learning, generative AI, and agentic frameworks. This role requires a hands-on professional who is detail-oriented, innovative, and thrives in a fast-paced environment where ideas rapidly evolve from prototypes to scalable solutions. As an AI Engineer, you will collaborate closely with cross-functional teams—including AI Productization, Data, and Engineering—to design, develop, and optimize intelligent systems that enhance our platform’s capabilities. Your work will directly impact our ability to deliver automation, scalability, and decision intelligence to enterprises worldwide, making this role vital to our ongoing success and innovation strategy.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related fields.
- At least 3-4 years of hands-on experience in AI/ML, Large Language Models (LLMs), or Generative AI application development.
- Experience with Retrieval-Augmented Generation (RAG) techniques and agentic frameworks is essential.
- Prior experience in fine-tuning models such as OpenAI, LLaMA, Mistral, Falcon, or similar platforms is highly preferred.
- A strong portfolio or GitHub repository showcasing AI or Generative AI projects will be considered an advantage.
- Proficiency in programming languages like Python, with familiarity in frameworks such as LangChain, Autogen, Hugging Face Transformers, and others.
- Excellent problem-solving skills, a passion for emerging AI technologies, and the ability to work collaboratively in a team environment are critical for success in this role.
Responsibilities
AI/ML Model Development
- Design, build, and optimize supervised and unsupervised machine learning models using Python and relevant libraries like Scikit-Learn.
- Conduct feature engineering, data wrangling, and comprehensive model evaluation using metrics such as ROC-AUC, F1 score, RMSE, Precision, and Recall.
- Work closely with Data and Engineering teams to ensure models are reproducible, scalable, and ready for deployment.
Generative AI & LLM Engineering
- Develop and integrate LLM-based applications utilizing frameworks like LangChain, Autogen, or LangGraph.
- Perform fine-tuning and instruction-tuning of models using tools such as Hugging Face Transformers, PEFT, LoRA, or OpenAI APIs.
- Optimize prompts, model parameters, and responses to enhance factual accuracy and contextual relevance.
- Implement RAG pipelines with vector databases like Pinecone, FAISS, Chroma, or Weaviate.
- Build evaluation pipelines to assess the quality, coherence, and bias of LLM outputs.
AI Agent Development
- Design and deploy autonomous AI agents capable of reasoning, planning, and multi-step tool utilization.
- Leverage frameworks such as LangGraph, Autogen, or CrewAI to develop multi-agent systems.
- Integrate agents with APIs, databases, and internal platforms to automate workflows effectively.
- Enhance the reliability, scalability, and maintainability of deployed AI systems.
Continuous Improvement & Documentation
- Maintain comprehensive documentation and model tracking to ensure reproducibility and transparency.
- Collaborate with cross-functional teams for seamless integration of AI solutions into customer offerings.
- Participate in peer reviews and sprint retrospectives to uphold quality standards and improve delivery efficiency.
- Stay updated with emerging AI/ML and agentic advancements to continually enhance our AI stack.
Benefits
Sutra.AI offers a competitive package with opportunities for professional growth and development. Our employees enjoy a collaborative and innovative work environment, flexible working hours, and the chance to work on cutting-edge AI projects that have a tangible impact on enterprise decision-making. We provide comprehensive health benefits, performance bonuses, and learning allowances to foster continuous skill enhancement. Additionally, we promote a healthy work-life balance and encourage participation in industry conferences, workshops, and training programs to ensure our team remains at the forefront of AI technology.
Equal Opportunity
Sutra.AI is an equal opportunity employer committed to fostering an inclusive environment for all employees. We celebrate diversity and are dedicated to creating a workplace free from discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender
Key skills/competency
- AI Engineer
- Machine Learning
- Generative AI
- LLMs
- Agentic Frameworks
- Python
- LangChain
- Autogen
- RAG
- Model Fine-tuning
Skills & topics
- AI Engineer
- Machine Learning
- Generative AI
- LLM
- Python
- LangChain
- Autogen
- RAG
- Artificial Intelligence
- SaaS
How to get hired
- Tailor your resume: Highlight AI/ML, LLM, and Generative AI experience.
- Showcase projects: Link your GitHub or portfolio with AI/GenAI work.
- Highlight skills: Emphasize Python, LangChain, Autogen, and RAG expertise.
- Prepare for interviews: Expect questions on ML models and AI agent development.
- Understand the company: Research Sutra.AI's mission and AI solutions.
Technical preparation
Behavioral questions
Frequently asked questions
- What are the key technical skills required for an AI Engineer at Sutra.AI?
- The AI Engineer role at Sutra.AI requires strong proficiency in Python, experience with machine learning libraries (like Scikit-Learn), and expertise in generative AI and LLM development using frameworks such as LangChain, Autogen, and Hugging Face Transformers. Experience with RAG techniques and fine-tuning models is also crucial.
- What kind of AI projects can I expect to work on as an AI Engineer at Sutra.AI?
- As an AI Engineer at Sutra.AI, you will work on designing and optimizing ML models, developing LLM-based applications, implementing RAG pipelines, and building autonomous AI agents. These projects aim to transform data into actionable insights and enhance enterprise decision-making processes.
- Does Sutra.AI encourage continuous learning for AI Engineers?
- Yes, Sutra.AI strongly encourages continuous learning. They offer learning allowances and promote participation in industry conferences, workshops, and training programs to ensure their AI Engineers stay at the forefront of AI technology and continuously enhance the AI stack.
- What is the importance of RAG and agentic frameworks for the AI Engineer role at Sutra.AI?
- Experience with Retrieval-Augmented Generation (RAG) techniques and agentic frameworks is essential for the AI Engineer role at Sutra.AI. These technologies are key to developing intelligent systems that can access and process information effectively, enhancing the capabilities of LLM-based applications and AI agents.
- How does Sutra.AI ensure AI models are production-ready?
- Sutra.AI ensures AI models are production-ready through close collaboration with Data and Engineering teams, focusing on reproducibility, scalability, and robust model evaluation using relevant metrics. They also emphasize continuous improvement and documentation throughout the development lifecycle.