Job Description
Type: FTE, Direct Hire is the preferred but is open to a 6-month W2 Contract-to-Hire arrangement if that’s the candidate’s preference.
(Not open to 3rd Party Candidates / Visa Sponsorship or Transfer is not available)
Work Location: Remote but have to be in the Southeast (preferably in Metro Atlanta) – with occasional trips to Atlanta office
Key Must-haves Skills / Experience
- 3 to 5+ years building and deploying ML systems.
- Python and libraries: PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
- 2+ years of hands-on experience with LLMs / SLMs: fine-tuning, prompt engineering, inference optimization.
- Experience with at least two: OpenAI GPT, Anthropic Claude, Google Gemini, Meta LLaMA.
- Vector databases, embeddings, and RAG pipelines.
- Skilled with structured/unstructured data, SQL, and distributed frameworks (Spark, Ray).
- Solid understanding of the full ML lifecycle
Position Overview
We are seeking a talented and forward-thinking Machine Learning Engineer with hands-on experience in LLMs, SLMs, GenAI, and agentic architectures to join our client’s expanding R&D and Applied AI team. In this role, you will be developing the next generation of agentic AI systems.
The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, along with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).
You will work to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role that combines building and scaling ML systems while contributing to cutting-edge applied research in agentic AI.
Responsibilities - Design, train, fine-tune, and deploy ML/LLM models for production.
- Build RAG pipelines using vector databases.
- Prototype and optimize multi-agent workflows using frameworks like LangChain, LangGraph, MCP.
- Develop prompt engineering strategies, optimization, and safety techniques for agentic LLM interactions.
- Integrate memory, evidence packs, and explainability modules into agentic pipelines.
- Work hands-on across multiple LLM ecosystems:
- OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs)
- Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows)
- Google Gemini (multimodal reasoning, advanced RAG integration)
- Meta LLaMA (fine-tuned/custom models for domain-specific tasks)
- Partner with Data Engineering to build and maintain real-time and batch data pipelines for ML/LLM workloads.
- Perform feature engineering, preprocessing, and embeddings generation for structured and unstructured data.
- Implement model monitoring, drift detection, and retraining pipelines.
- Utilize cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.
- Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.
- Experiment with generative AI and multimodal models to expand capabilities beyond text (e.g., images, structured data).
- Collaborate with R&D teams to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.
- Translate research prototypes into production-ready components.
- Work cross-functionally with R&D, Data Science, Product, and Engineering teams to deliver business-aligned AI features.
- Participate in design reviews, architecture discussions, and model evaluations.
- Document experiments, processes, and results for knowledge sharing.
- Mentor junior engineers and contribute to ML engineering best practices.
Required Qualifications
- 3 to 5+ years of experience building & deploying Machine Learning systems.
- Proficient in Python and libraries like PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
- 2+ years of hands-on experience with LLMs / SLMs, including prompt design, fine-tuning, inference optimization.
- Demonstrated experience with at least two of the following: OpenAI GPT models, Anthropic Claude, Google Gemini, or Meta LLaMA
- Familiar with vector databases, embeddings, and RAG pipelines.
- Skilled in handling structured and unstructured data at scale.
- Knowledge of SQL and distributed data frameworks (Spark, Ray).
- Strong understanding of the ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
Preferred Qualifications
- Experience with agentic frameworks, such as LangChain, LangGraph, MCP, AutoGen.
- AI safety, explainability, and guardrails knowledge.
- Experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
- MLOps, CI/CD for ML, monitoring, and observability experience.
- Exposure to anomaly detection, risk modeling, or behavioral analytics.
Pay
The compensation consists of a
Salary ranging from $140,000 to $160,000 per year ($65 to $75/hr W2 if Contract/C2H).
The disclosed pay range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. The compensation decisions are dependent on the facts and circumstances of each case, such as skills and experience levels.
