1 day ago
Chennai, IndiaStaff+
Responsibilities
- Design and develop algorithms and AI solutions using LLMs, NLP, deep learning, generative AI, information retrieval, and feedback-based techniques.
- Design agent architectures involving planning, tool use, memory, orchestration, multi-step reasoning, feedback loops, and execution control.
- Develop data-processing and representation techniques for transforming unstructured data into structured formats for modeling, retrieval, and analysis.
- Build evaluation frameworks and benchmarks measuring accuracy, relevance, groundedness, helpfulness, safety, latency, and cost.
- Optimize models through prompt engineering, retrieval optimization, reranking, embeddings, model selection, and fine-tuning.
- Design AI guardrails and mitigation approaches, including validation, hallucination mitigation, policy adherence, responsible AI, and human oversight.
- Translate business requirements into applied AI approaches, algorithm designs, experiments, and measurable outcomes.
- Lead technical discussions and mentor team members on algorithms, modeling, experimentation, and AI best practices.
Requirements
- 6–8 years of professional experience in applied AI/ML, data science, machine learning, algorithms, statistics, or a related technical field.
- Bachelor's or master's degree in Engineering, Data Science, Computer Science, or a related field.
- Hands-on experience developing and evaluating machine learning and NLP solutions involving transformers, deep learning, embeddings, information retrieval, classification, ranking, summarization, question answering, and agent decision-making.
- Experience with LLMs and foundation models such as OpenAI, Anthropic, Cohere, Llama, or equivalent technologies.
- Experience building generative AI and agentic AI solutions using LangChain, LangGraph, RAG, prompt engineering, MCP, or equivalent frameworks.
- Experience evaluating LLM and agent performance across accuracy, relevance, groundedness, helpfulness, safety, latency, and cost.
- Experience designing AI guardrails and applying responsible AI practices, including hallucination mitigation, output validation, policy adherence, and human oversight.
- Ability to identify emerging AI technologies and apply them to meaningful business use cases.
- Strong communication and collaboration skills, including explaining complex technical concepts to technical and non-technical stakeholders.
Benefits
- Hybrid work environment with flexibility to work virtually when productive and collaborate in person in an engaging community.
- World-class benefits, competitive compensation, and performance-based rewards.
- Professional growth opportunities, inclusive teams, and access to Gartner's AI expertise, resources, and technology.
Categories
About Gartner
Gartner is a public research and advisory firm that sells subscription research, consulting, and conferences to CIOs and business leaders at large enterprises and governments. Its offerings include market analysis (e.g., Magic Quadrant), peer benchmarks, expert advisory, and events. Founded in 1979 and headquartered in Stamford, Connecticut, Gartner trades on the NYSE (ticker: IT) and is a member of the S&P 500.