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GEN AI Architect

GEN AI Architect

Tata Consultancy Serviceschennai, India
1 day ago
Job description

Responsibility of / Expectations from the Role

EXP : 8 TO 10

  • Data mining or extracting usable data from valuable data sources.
  • Using machine learning tools to select features, create and optimize predictive models.
  • Carrying out preprocessing of structured and unstructured data
  • Enhancing data collection procedures to include all relevant information for developing analytic systems.
  • Processing, cleansing, and validating the integrity of data to be used for analysis.
  • Analyzing large amounts of information to find patterns and solutions.
  • Developing prediction systems and machine learning algorithms
  • Ability to use LLMs to build GenAI powered solutions.
  • Presenting results in a clear manner
  • Propose solutions and strategies to tackle business challenges.
  • Collaborate with Business and IT teams.
  • Designing and developing machine learning and deep learning systems
  • Knowledge of productionizing the ML and DL applications and monitoring

Must-Have

  • Gen AI
  • Experience in LLMs, Embedding Models, Prompt Engineering
  • Knowledge of statistical programming languages like Python
  • Good applied statistical skills
  • Good knowledge of machine learning algorithms
  • Proficiency in handling imperfections in data & generating insights and patterns.
  • Experience with Data Visualization Tools
  • Fine tuning of LLM
  • Evaluation Framework (RAGAS, ROUGE, BLUE Scrore)
  • Autoencoding model (Encoder : ROBERTA / BERT / DistilBERT)
  • Langsmith framework
  • Autoregressive model (Decoder : GPT / BLOOM / LLama / Mistral / Claude / CodeGen / OPT / PaLM
  • RAG / Multimodal Architecture using Langchain / LlamIndex
  • MCP Architecture
  • Experience Agentic AI Framework ( Crew AI, AutoGen, LangGraph, Google AVD etc. )
  • Experience in Model deployment, LLMOps, Scalability and Performance
  • Fine tuning of LLM
  • AI ML exp.
  • Candidate has exp. in Transformer Architecture (Encoder and decoders)
  • Knowledge Graph implementation (If anyone has created knowledge graph, that's good)
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