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Chimera Technologies - Artificial Intelligence Lead - LLM

Chimera Technologies - Artificial Intelligence Lead - LLM

CHIMERA TECHNOLOGIES PRIVATE LIMITEDBangalore
30+ days ago
Job description

Job Title : AI Lead

Experience Level : 6- 8 Years

Location : HSR Layout, Bangalore

Job Type : Permanent (Full-time)

About the Role :

We are seeking a highly skilled AI Lead to architect, develop, and scale AI-driven solutions, with a strong focus on LLM (Large Language Model) orchestration, model deployment, and end-to-end AI system design. This role requires deep technical expertise, leadership ability, and hands-on experience across the AI / ML lifecycle from data ingestion to model serving along with the capability to lead and mentor a team in delivering cutting-edge AI solutions for diverse industry use cases.

Key Responsibilities :

  • AI System Architecture : Design scalable and modular AI pipelines covering data ingestion, preprocessing, model training / fine-tuning, retrieval-augmented generation (RAG), and inference layers.
  • Model Development & Deployment :

1. Develop, fine-tune (PEFT), and deploy models using PyTorch or TensorFlow.

2. Leverage LangChain / LlamaIndex for LLM orchestration and HuggingFace Transformers for NLP and generative AI tasks.

3. Integrate and optimize LLM APIs such as OpenAI, AWS Bedrock, Vertex AI, Azure OpenAI.

  • Data Handling :
  • 1. Work with Vector Databases (Weaviate, Milvus, FAISS, MongoDB Atlas Vector Search) for semantic search and RAG pipelines.

    2. Manage structured and unstructured data using PostgreSQL, MongoDB, Redis, DynamoDB.

    3. Optimize data retrieval performance with caching strategies.

  • Backend & API Development : Build high-performance ML inference APIs using FastAPI or Flask, integrating with Kafka or RabbitMQ for real-time processing.
  • DevOps & MLOps :
  • 1. Containerize applications using Docker.

    2. Implement CI / CD pipelines with GitHub Actions, GitLab CI, or Jenkins.

    3. Track experiments and manage models using MLflow or Weights & Biases.

    Storage & Search :

  • Manage storage of model artifacts and datasets in AWS S3 / Google Cloud Storage.
  • Implement intelligent search over structured and unstructured text with ElasticSearch / OpenSearch.
  • Code Quality & Documentation : Enforce linting, testing, type hinting, and structured config management (Pytest, Black, Ruff, Flake8, YAML / JSON).
  • Prompt Engineering & Evaluation : Design, refine, and evaluate prompts; establish evaluation metrics and benchmarking frameworks.
  • Team Leadership : Mentor engineers, review architectures, manage timelines, and engage with stakeholders to align AI initiatives with business goals.
  • Required Technical Expertise :

  • Programming : Advanced proficiency in Python with clean coding practices.
  • LLM & NLP : Hands-on experience with LangChain, LlamaIndex, HuggingFace, RAG pipelines.
  • Databases : Deep understanding of relational, NoSQL, and vector databases for different retrieval use cases.
  • System Integration : Expertise in Kafka / RabbitMQ for asynchronous processing and scalable AI workloads.
  • Cloud & APIs : Experience with AWS, GCP, or Azure AI / ML services and REST API development.
  • Model Lifecycle Management : Skilled in training, fine-tuning, deployment, monitoring, and scaling of AI models.
  • Qualifications :

  • Bachelors or Masters degree in Computer Science, AI / ML, Data Science, or related field.
  • 6- 8 years of hands-on AI / ML experience with at least 3 years in leading teams and projects.
  • Proven track record in delivering end-to-end AI systems in production environments.
  • Strong architectural decision-making skills with an emphasis on scalability, performance, and maintainability.
  • Excellent communication skills for client engagement, technical presentations, and documentation.
  • Preferred (Nice-to-Have) :

  • Prior experience in pre-training large models.
  • Contributions to open-source AI / ML projects.
  • Knowledge of distributed AI training strategies.
  • Experience in multi-cloud deployments and hybrid infrastructure setups.
  • (ref : hirist.tech)

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