Summary: The role is for an AI/ML Engineer with a focus on Agentic AI, requiring extensive experience in AI and ML implementations. The position demands strong communication skills and expertise in prompt engineering, machine learning, and software engineering. Candidates should have hands-on experience with modern LLMs and knowledge of computer vision projects. This is a contract position based remotely in the US.
Key Responsibilities:
- Implement AI and ML use cases with a focus on prompt engineering.
- Design and optimize prompts for various AI models.
- Build conversational flows for chatbots.
- Work with embedding models and vector databases.
- Develop and optimize end-to-end ML systems.
- Integrate LLM prompts into production applications.
Key Skills:
- Agentic AI experience.
- Strong communication skills.
- 8+ years of experience in AI and ML.
- Hands-on experience with modern LLMs (OpenAI, Anthropic, LLaMA, Mistral, Gemini).
- Proficiency in Python, PyTorch/TensorFlow.
- Experience with REST APIs, JSON, YAML.
- Familiarity with Git and MLOps practices.
Salary (Rate): undetermined
City: undetermined
Country: US
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Detailed Description From Employer:
Position: AI /ML engineer with Agentic AI experience
Location: Remote, US (2 positions)
Type: Contract
Exp: 13+ Years
Must Have: Agentic AI experience & Very strong communication.
Job Profile:
An expert Prompt engineer with a strong software engineering a background and an excellent communicator with 8+ years of experience implementing AI and ML use cases. (Primarily AI)
Knowledge of Computer vision related projects.
LLM Expertise:
- Must have hands-on experience working with modern LLMs (OpenAI, Anthropic, LLaMA, Mistral, Gemini),as well as a strong understanding of tokenization, model behaviors, reasoning patterns, and evaluation frameworks.
- Prompt Design & Optimization (zero-shot, few-shot, chain-of-thought, ReAct, self-consistency)
- Structured prompt templates
- Refinement (prompt chaining, decomposition, and verification strategies)
- Safety, Guardrails & Compliance
- Experience building conversational flows (chatbots).
Retrieval-Augmented Generation (RAG) :
- Work with embedding models, vector databases, context windows, and chunking strategies
- Work experience in Vector database (FAISS, Milvus, Pinecone or any)
Machine Learning:
- Machine Learning Engineer with strong experience in building, deploying, and optimizing end-to-end ML systems.
- Skilled in data preprocessing, feature engineering, model development, and production deployment using modern ML frameworks.
- Proficient in Python, PyTorch/ TensorFlow, cloud services, and MLOps practices.
Software Engineering:
- Python proficiency.
- Very good understanding and work experience with REST APIs, JSON, YAML.
- Ability to integrate LLM prompts into production applications.
- Familiarity with Git, version control, and experiment tracking.
