AI Specialist

Responsibilities

• AI Model Development: Design, train, and optimize AI/ML models for NLP, computer vision, and generative AI tasks.
• AI Agents & Automation: Develop autonomous AI agents capable of decision‑making, reasoning, and real‑time data processing.
• Vector Databases & AI Search: Implement and optimize vector search solutions using FAISS, Pinecone, Weaviate, or Milvus for AI‑powered search and retrieval.
• Text‑to‑Video & Generative AI: Work on AI‑driven text‑to‑video generation, speech synthesis, and multimodal AI applications.
• Cloud Infrastructure (AWS): Deploy and manage AI models on AWS (Sage

  Maker, Bedrock, Lambda, S3, API Gateway, Dynamo

  DB, Step Functions).
• Scalability & Performance Optimization: Ensure efficient AI model inference, distributed computing, and GPU acceleration (AWS Inferentia, Neuron SDK).
• Data Engineering for AI: Build and maintain data pipelines for AI/ML workloads, integrating real‑time streaming and batch processing.
• MLOps & CI/CD: Automate AI model training, deployment, and monitoring using AWS Sage

  Maker Pipelines, Terraform, Docker, and Git

  Hub Actions.
• Security & Compliance: Implement best practices for AI model security, data governance, and responsible AI principles.
• Collaboration: Work closely with software engineers, data engineers, and product teams to integrate AI solutions into production applications.

Requirements

• 8+ years of experience in AI/ML development, with a focus on building and deploying AI‑powered applications.
• Expertise in Python (Py

  Torch, Tensor

  Flow, Hugging Face, Lang

  Chain, Open

  AI APIs).
• Strong experience with AWS AI/ML services (Sage

  Maker, Bedrock, Lambda, Step Functions, Dynamo

  DB).
• Hands‑on expertise in vector databases (FAISS, Pinecone, Weaviate, Milvus) for AI‑powered search and retrieval.
• Experience developing AI Agents (Lang

  Chain, Auto

  GPT, Open

  AI, RAG‑based systems).
• Familiarity with text‑to‑video AI tools (RunwayML, Pika Labs, Stability AI).
• Deep knowledge of LLM fine‑tuning, prompt engineering, and retrieval‑augmented generation (RAG).
• Proficiency in MLOps best practices (CI/CD pipelines, model monitoring, AI observability).
• Experience with real‑time AI workloads (Kafka, AWS Kinesis, event‑driven architectures).
• Strong background in distributed computing and GPU‑accelerated AI (CUDA, AWS Inferentia, Neuron SDK).

Preferred Qualifications

• Experience with reinforcement learning (RLHF), autonomous agents, and decision‑making AI.
• Knowledge of synthetic data generation for AI model training.
• Understanding of AI model compression (quantization, pruning, distillation) for edge AI applications.
• Experience in multi‑modal AI models integrating text, video, and speech synthesis.

Preferred Skills

• Knowledge of Reinforcement Learning, Generative AI, or Explainable AI (XAI).
• Experience in Vector Databases, Retrieval‑Augmented Generation (RAG), and AI model optimization.
• Familiarity with GPU acceleration, CUDA, and edge AI deployment.
• Understanding of AI ethics, bias mitigation, and regulatory compliance.