Job description

SentientGeeks is seeking a highly skilled and motivated AI/ML Engineer to join our engineering team. This role focuses on the convergence of Generative AI and Computer Vision to architect and deploy sophisticated "agentic" systems. The successful candidate will be responsible for developing autonomous AI workflows, integrating advanced visual perception capabilities, and delivering scalable enterprise-grade solutions. You will work at the intersection of research and production, transforming complex business requirements into high-impact AI applications.

Key Responsibilities

Agentic System Development: Design and implement intelligent agent architectures capable of autonomous reasoning, task planning, and execution.
Computer Vision Integration: Leverage cutting-edge models (e.g., YOLO, Vision Transformers) to enable AI agents to interpret, analyze, and process visual data effectively.
Knowledge Retrieval Optimization: Architect and maintain advanced Retrieval-Augmented Generation (RAG) pipelines, ensuring seamless integration of multi-modal data for context-aware responses.
Workflow Orchestration: Utilize industry-standard frameworks, including LangChain and LangGraph, to build modular, resilient, and scalable AI service chains.
Cloud Deployment & Scalability: Manage the end-to-end lifecycle of AI solutions, including deployment, monitoring, and performance optimization on cloud infrastructure (AWS, Azure, or GCP).
Cross-Functional Collaboration: Collaborate with product and engineering teams to translate abstract business objectives into actionable technical prototypes and production-ready systems.

Technical Requirements

Experience: 2–3 years of professional experience in software development with a dedicated focus on AI/ML lifecycle management.
Programming: Mastery of Python for robust, production-quality code.
AI/ML Foundational Knowledge: Proven expertise in LLMs, RAG, and Computer Vision architectures.
Frameworks & Tooling:
Orchestration: Hands-on experience with LangChain or LangGraph.
Vision Libraries: Proficiency in OpenCV, PyTorch, YOLO, Detectron2, SAM, or ViT.
Database Management: Experience with vector databases such as Chroma, Pinecone, or FAISS.
Cloud Infrastructure: Demonstrated experience with major cloud platforms (AWS, Azure, or GCP).
Problem-Solving: Proven ability to decompose complex technical requirements into scalable, iterative development milestones.

Preferred Qualifications

Candidates possessing the following attributes will be given priority:

Production Lifecycle: Experience transitioning models from development environments (Jupyter/Colab) to live, high-concurrency APIs.
Edge AI & Inference: Proficiency in model optimization techniques such as ONNX or TensorRT for low-latency inference.
MLOps Proficiency: Experience with containerization (Docker/Kubernetes) and tracking tools (MLflow, Weights & Biases).
Advanced Training: Proven experience with Parameter-Efficient Fine-Tuning (PEFT/LoRA) and custom training on specialized datasets.
Multi-Modal Expertise: Practical application of modern multi-modal architectures (e.g., Llama Vision, Claude 3.5 Sonnet, GPT-4o).
Community Engagement: Evidence of active contributions to open-source AI projects or a strong portfolio of independent AI/ML developments on GitHub.

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