Senior Engineer - Generative AI
Pune Full-time Senior AI Engineering
At a glance
Senior AI Engineering role at Ac (Ec). Pune · full-time.
Pay not stated AI Engineering salary
RoleAI Engineering
SenioritySenior
LocationPune
EmploymentFull-time
PostedOct 5, 2026 · 2w ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Build, test and launch generative AI systems for specific use cases, including solutions using online or offline language models
- Create multi-step agent workflows and connect them to knowledge bases and outside data through retrieval systems
- Develop data pipelines that prepare, index and retrieve text, tables, documents and images
- Improve models through fine-tuning, prompt design, evaluation, A/B tests and repeated iteration
- Assess LLM and chat-system vulnerabilities, then work with platform teams to monitor, secure and scale deployments
What you bring
- You have four to six years in software engineering and AI-related work, including at least two to three years directly with generative AI and LLMs
- You work confidently in Python and have hands-on experience with LLMs, RAG, vector databases and retrieval pipelines
- You have deployed tool-enabled agent workflows and used Garak or a comparable scanner to find and address model weaknesses
- You have built content moderation systems and can deploy AI services using cloud platforms, APIs and containers
Who this fits
This senior engineering role suits someone who can turn business needs into AI system designs with clear performance, cost and safety limits. The role is based in Pune, Ahmedabad or Indore, and calls for a notice period of zero to thirty days.
From the employer
Position:
Senior Engineer - Generative AIJob Description:
Role: Senior Generative AI / Agentic AI Engineer
Location - Ahmedabad, Indore, Pune
Years of Experience: 4 -7 Yrs
Notice - 0-30 Days
Role summary:
- Senior-level engineer focused on designing, building, and deploying production-grade generative AI and agentic-AI solutions.
- Responsible for delivering secure, scalable, and business-oriented AI systems that operate on structured and unstructured data and enable AI-driven decision-making
Required experience
- 4–6 years of industry experience in software engineering and AI-related roles.
- Minimum 2-3 years of direct experience with Generative AI and Large Language Models (LLMs).
Key Responsibilities:
- Architect, develop, test, and deploy generative-AI solutions (online/offline LLMs, SLMs, TLMs) for domain-specific use cases.
- Design and implement agentic AI workflows and orchestration using frameworks such as LangGraph, Crew AI, or equivalent.
- Integrate enterprise knowledge bases and external data sources via vector databases and Retrieval-Augmented Generation (RAG).
- Build and productionize ingestion, preprocessing, indexing, and retrieval pipelines for structured and unstructured data (text, tables, documents, images).
- Implement fine-tuning, prompt engineering, evaluation metrics, A/B testing, and iterative model improvement cycles.
- Conduct/model red-teaming and vulnerability assessments of LLMs and chat systems using tools like Garak (Generative AI Red-teaming & Assessment Kit).
- Collaborate with MLOps/platform teams to containerize, monitor, version, and scale models (CI/CD, model registry, observability).
- Ensure model safety, bias mitigation, access controls, and data privacy compliance in deployed solutions.
- Translate business requirements into technical designs with clear performance, cost, and safety constraints.
Required Skills and Experience:
- Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
- Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.
- Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
- Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
- Demonstrated experience implementing content filtering / moderation systems.
- Solid skills working with structured and unstructured data and advanced feature engineering.
- Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
- Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
- Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
- Good knowledge of security, data governance, and privacy best practices for AI systems.
Preferred Qualifications
- Hands-on fine-tuning experience and parameter-efficient tuning methods.
- Experience with multimodal models and retrieval-augmented multimodal pipelines.
- Prior work on agentic safety, tool-use constraints, LLM application firewalls, or human-in-the-loop systems.
- Familiarity with LangChain, LangGraph, Crew AI, or similar orchestration libraries.
Values & behaviours
- AI-first thinking: consistently seeks AI-enabled solutions to business problems.
- Data-driven mindset: makes decisions based on measurable insights and metrics.
- Collaboration & agility: effective contributor in cross-functional, fast-paced teams.
- Problem-solving orientation: looks beyond the obvious to unlock product and business value.
- Business impact focus: designs solutions with measurable outcomes and real adoption.
- Continuous learning: stays current with academic research, open-source tooling, and best practices.