Machine Learning Engineer
Job Description
Our client, an innovation-driven company based in Washington, DC, has partnered with Talentra to find a "Machine Learning Engineer" who will play a key role in advancing their mission to make organizations more knowledge-efficient.
By combining patented AI, data science, and human expertise, the company delivers fast, actionable intelligence that empowers smarter decision-making across industries.
This role will contribute directly to building and scaling intelligent systems across their product stack, particularly in the areas of agentic workflows, graph-based knowledge systems, and retrieval-augmented generation (RAG).
You’ll be responsible for designing, fine-tuning, and deploying advanced ML models that integrate seamlessly with large language models and graph databases, turning complex data into usable, real-time insights.
Key Responsibilities
Fine-tune Sentence Transformer models using custom loss functions and targeted training strategies
Build agentic workflows using Langraph, DSPy, and RAG with conversational memory handling
Deploy and monitor ML pipelines on AWS using SageMaker, Bedrock, and related tools
Drive continuous improvement through robust data transformation and feedback loops
Integrate with graph/vector databases (e.g., Neo4j, Weaviate) to power semantic features
Collaborate closely with backend and product teams to bring ML capabilities into production
Oversee performance tuning, model governance, and cost optimization in live systems
Ideal Profile – Must Have
8+ years in Data Science or Machine Learning roles
Master’s degree in AI, Data Science, Mathematics, or a related field
Strong hands-on experience with AWS MLOps (SageMaker, Bedrock)
Expertise in fine-tuning Sentence Transformers, including custom loss functions (e.g., Triplet, Contrastive), and deep understanding of embedding space behavior and agentic tools (Langraph, DSPy), and vector databases
Proficient in Python, PyTorch, HuggingFace, LangChain, and model evaluation/deployment
Deep understanding of RAG pipelines, vector-based retrieval, and conversational memory design.
Bonus Skills
Experience with tool-calling in LLMs and open-source agent frameworks
Familiarity with knowledge graphs, secure inference, or ontology modeling
Background in managing or mentoring engineering teams
If you're passionate about building real-world AI solutions at the intersection of LLMs, semantic systems, and cutting-edge infrastructure, this role offers a rare opportunity to do exactly that, within a remote-first, globally respected team.
What you'll do
- Design, train, and evaluate large language models and agentic pipelines
- Build production-grade inference and orchestration systems
- Collaborate with product and engineering teams to ship AI features end-to-end
- Run experiments, benchmark models, and iterate on prompt and fine-tuning strategies
- Own model quality, latency, and cost across the stack
- Mentor engineers and contribute to internal ML platform improvements
What we're looking for
- Strong background in machine learning, NLP, or deep learning
- Hands-on experience with PyTorch, Hugging Face, or similar frameworks
- Experience deploying models to production
- Solid Python engineering skills
- Familiarity with RAG, vector databases, and agent frameworks
- Strong communication skills in English
Nice to have
- Published research or open-source contributions
- Experience with distributed training or GPU optimization