Senior GenAI Engineer

Accountabilities:

- Architect and build scalable Generative AI and agentic AI applications from initial concepts and prototypes through production deployment.

- Design LLM-powered workflows, prompt strategies, reflexive systems, self-learning architectures, and multi-agent solutions.

- Develop intelligent AI agents using LangChain, LangGraph , or comparable frameworks for use cases including NL-to-SQL, autonomous task execution, and RAG pipelines.

- Evaluate, select, customize, fine-tune, and optimize state-of-the-art large language models for specific business and technical requirements.

- Design and own end-to-end ML and GenAI pipelines , covering training, deployment, monitoring, and lifecycle management.

- Build APIs, microservices, and integration frameworks that embed AI capabilities into enterprise products and workflows.

- Establish responsible AI practices focused on mitigating hallucinations, bias, reliability, security, and other AI-related risks.

- Collaborate directly with customers, product teams, and engineering stakeholders to translate business requirements into robust AI architectures and solutions.

- Design distributed, cloud-native systems that can support scalable enterprise AI workloads.

- Mentor engineers, share technical expertise, and contribute to long-term AI platform strategy.

- Stay current with emerging developments in Generative AI, agentic systems, LLMs, and AI engineering practices.

Requirements:

- 6+ years of experience in traditional machine learning , including at least 2 years of hands-on Generative AI experience .

- Strong practical knowledge of LLMs, GPT-based models, prompt engineering, Generative AI, and agentic AI systems .

- Real-world experience with LangChain, LangGraph , or similar agentic AI frameworks.

- Strong Python development skills, including API wrappers, third-party integrations, automation, and internal tooling.

- Solid understanding of Transformers, CNNs, and RNNs , with hands-on experience using TensorFlow, PyTorch, and Scikit-learn .

- Experience with NLP, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) .

- Hands-on experience with models and platforms such as OpenAI, Llama/Llama 2, Azure OpenAI , and other open-source or commercial LLMs.

- Experience designing distributed and cloud-native architectures using microservices and REST APIs .

- Proficiency with at least one major cloud platform, including AWS, Azure, or GCP , as well as Docker and Kubernetes .

- Practical experience with MLOps and LLMOps , including model training, deployment, monitoring, and lifecycle management.

- Excellent communication skills, with the ability to translate complex technical concepts into clear recommendations for non-technical stakeholders.

- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics , or a related discipline.

- Strong ownership mentality and the ability to thrive in a fast-paced, startup-oriented environment.

- Preferred experience includes LLM fine-tuning using LoRA, RLHF, or PEFT .

- Familiarity with performance optimization techniques such as GPU/TPU acceleration, quantization, pruning, and knowledge distillation .

- Experience with AI observability and monitoring tools, as well as familiarity with AI governance and compliance frameworks such as GDPR and SOC 2 , is advantageous.

- Prior consulting or solution architecture experience delivering enterprise AI products and exposure to financial services, healthcare, or insurance are considered valuable.

Benefits:

- Full-time, fully remote position within India.

- Opportunity to work on next-generation Generative AI and agentic AI products .

- Hands-on exposure to advanced LLMs, multi-agent architectures, RAG, and AI-powered enterprise solutions.

- Customer-facing responsibilities providing direct exposure to real-world AI transformation initiatives.

- Significant ownership across AI architecture, engineering, deployment, and platform strategy.

- Opportunity to work with modern technologies including LangChain, LangGraph, cloud platforms, Docker, Kubernetes, MLOps, and LLMOps .

- Collaborative environment with opportunities to mentor engineers and influence technical direction.

- Exposure to responsible AI, AI observability, governance, and enterprise-grade AI practices.

- Strong opportunities for technical growth and professional development in a rapidly evolving AI landscape.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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