AI Lead
Akrys.ai · Bengaluru · onsite · Posted 49d ago
Not disclosed6–10 yrsfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
The core requirements for the job include the following:
AI / GenAI / LLM:
- Strong hands-on experience with Large Language Models (LLMs) such as GPT, Claude, and Gemini.
- Experience in building generative AI applications using RAG (Retrieval-Augmented Generation).
- Expertise in prompt engineering, prompt tuning, and evaluation frameworks.
- Experience with fine-tuning techniques such as LoRA, PEFT, and model optimization.
- Hands-on experience with OpenAI, Azure OpenAI, AWS Bedrock, or similar APIs.
Agentic AI:
- Experience in building AI agents and multi-agent systems.
- Hands-on experience with LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, and Semantic Kernel.
- Understanding of tool calling, function calling, and agent orchestration.
- Experience with memory systems, context handling, and reasoning workflows.
- Ability to design planning agents and autonomous decision-making systems.
Architecture and Engineering:
- Strong experience in AI system architecture and scalable distributed systems.
- Experience with microservices architecture, REST APIs, and event-driven systems.
- Ability to design real-time and batch AI pipelines.
- Experience in building production-grade AI platforms and services.
MLOps / LLMOps:
- Experience with MLflow, Kubeflow, Databricks, and SageMaker.
- Strong understanding of CI/CD pipelines for AI systems.
- Experience in model deployment, monitoring, drift detection, and observability.
- Experience in model versioning and lifecycle management.
Data and Retrieval:
- Experience with vector databases such as FAISS, Pinecone, Weaviate, and Chroma.
- Strong understanding of embeddings, semantic search, and retrieval pipelines.
- Experience with structured and unstructured data processing.
- Knowledge of data pipelines and ETL processes.
Programming and Cloud:
- Strong proficiency in Python.
- Experience with PyTorch, TensorFlow, and Hugging Face Transformers.
- Experience with FastAPI or Flask for building AI services.
- Hands-on experience with Azure, AWS, or GCP.
- Experience with Docker and Kubernetes.
- Understanding of GPU-based workloads and scalable infrastructure.
Sourced from instahyre
Skills
Natural Language ProcessingLarge Language Models (LLMs)PythonRetrieval Augmented GenerationclaudegeminiLarge Language Modelgpt