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.

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