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Lead / Manager - AI Engineering

УдалённоIndia только
Опубликовано
Роль
AI / ML
Опыт
Лид
Занятость
Полная занятость
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Коротко по делу

Hands-on GenAI/Agentic AI lead or manager in Hyderabad with 5–10 years AI/ML experience and 2–3 years building Generative AI solutions. Must be strong in LLM systems, RAG, prompt engineering, evaluation design, Python, ML frameworks, Azure/AWS/Snowflake AI tools, and client-facing delivery.

Ключевые навыки

LLMPythonRAG

Обязательные навыки

Azure AISnowflakeMachine LearningPyTorchTensorFlowScikit-learnClaude CodeOpenAI CodexAzure OpenAIAWS BedrockSnowflake Cortex

Желательные навыки

AntiGravityCursorVS Code

Чем предстоит заниматься

  • Translate business needs into testable GenAI and Agentic Engineering solutions, clear outputs, and measurable success criteria; define scope boundaries, including risks.
  • Run feasibility assessments to choose the right approach: prompting vs RAG vs fine-tuning vs classical ML.
  • Select and develop models based on task requirements, working with AI Engineering to understand latency/cost, and risk profile.
  • Design prompting strategies: instruction design, few-shot sets, structured outputs, tool/agent prompts, and robustness patterns.
  • Implement as an MVP and iterate based on eval results.
  • Establish prompt iteration methodology driven by evals: prompt versioning, ablations, and change control.
  • Define the evaluation plan for GenAI systems and agentic workflows, designing and implementing evaluation from LLM as a judge and ensure evaluation includes fairness and bias considerations where applicable.
  • Define acceptance thresholds and release gates tied to these metrics.
  • Own experimentation and model improvements.
  • Run structured experiments across prompts, retrievers, chunking, models.
  • Develop methods for identifying model failures such as hallucination types, retrieval misses, instruction-following errors, formatting failures etc.
  • Provide recommendations for improvements grounded in evidence: what to change, expected lift, and trade-offs.
  • Deliver an engineering-ready handoff: prompt packages and versioning approach, RAG configuration, tool schemas, evaluation harness, datasets/ground truth, metric definitions, and go/no-go gates.
  • Design scalable and secure Agentic AI architectures adhering to best practices in data engineering, MLOps and LLMOps.
  • Partner AI engineering for LLM implementation needs by providing clear specs, eval harnesses, and acceptance thresholds.
  • Mentor DS/analysts on GenAI evaluation methods, labelling operations, and scientific rigor.
  • Collaborate with Product and Software Engineers for integrating AI capabilities into platforms and user-facing services.
  • Collaborate with DevOps/Platform Engineers for environment setup, monitoring, infrastructure, and reliability.
  • Collaborate with Data Engineering for designing and accessing upstream data pipelines.

Что требуется

  • 5-10 years of overall AI/ML experience out if which at least 2 to 3 years of Generative AI solutions.
  • Strong background in applied ML, data science, LLM and Agentic AI Engineering Systems with demonstrated delivery and client facing experience.
  • Deep expertise in evaluation design, metrics, and dataset curation for LLM systems.
  • Proven experience in model selection and prompt engineering, including structured output and tool-use prompting.
  • Strong proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn).
  • Strong experience in LLM fine-tuning, RAG Context Engineering, Claude Code, Open AI Codex, Agentic Workflows.
  • Strong RAG design choices (chunking, embeddings, retrieval strategies, reranking) and how to evaluate them.
  • Must have implemented Agentic AI SDLC.
  • Working with GenAI on Azure, AWS, or Snowflake involves leveraging cloud-native AI tools—such as Azure OpenAI, AWS Bedrock, or Snowflake Cortex—to build or consume intelligent solutions directly on governed data.
  • Preferred: Experience on vibe coding - such as AntiGravity, Cursor, and VS Code is highly desirable.
  • Proven ability to build end-to-end GenAI MVPs in Python (RAG/agents + evaluation harness) and prepare them for production handoff.
  • Excellent communication and stakeholder management skills with a strategic mindset.

Преимущества

  • Competitive Salary: Your skills and contributions are highly valued here, and we make sure your salary reflects that, rewarding you fairly for the knowledge and experience you bring to the table.
  • Dynamic Career Growth: Our vibrant environment offers you the opportunity to grow rapidly, providing the right tools, mentorship, and experiences to fast-track your career.
  • Idea Tanks: Innovation lives here. Our "Idea Tanks" are your playground to pitch, experiment, and collaborate on ideas that can shape the future.
  • Growth Chats: Dive into our casual "Growth Chats" where you can learn from the best—whether it's over lunch or during a laid-back session with peers, it's the perfect space to grow your skills.
  • Snack Zone: Stay fuelled and inspired! In our Snack Zone, you'll find a variety of snacks to keep your energy high and ideas flowing.
  • Recognition & Rewards: We believe great work deserves to be recognized. Expect regular Hive-Fives, shoutouts, and the chance to see your ideas come to life as part of our reward program.
  • Fuel Your Growth Journey with Certifications: We're all about your growth! Enhance your expertise with company-sponsored certifications in AI, Data Science, Cloud, and Analytics technologies.

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Что говорят о компании

3.8/ 5

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