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Generative AI Engineer

Generative AI Engineer

eInfochips (An Arrow Company)ahmedabad, gujarat, in
12 days ago
Job description

Role : Generative AI Engineer

Location : Ahmedabad, Indore, Pune

Experience : 4 to 7 Years

Notice Period : Immediate to 30 Days Joiner

Role summary :

  • Senior-level engineer (4–6 years of professional experience) 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.
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