Top AI & Machine Learning Jobs

The AI Engineer at BLEN will design and build agentic AI systems, including LLM-powered applications, integrate APIs, and ensure responsible AI practices for federal clients, focusing on performance and observability.

Lead a team of ML engineers to design, build, and productionize LLM/GenAI and agentic systems (retrieval, tool use, orchestration). Collaborate cross-functionally with MLOps, product, and data science, review code, mentor engineers, run technical evaluations and proofs-of-concept, and ensure reliable, safe ML deployments for a large SaaS product.

Lead the design and development of AI solutions in enterprise environments, combining hands-on engineering with mentorship and technical leadership.

Research-focused quantitative role developing time-series models and predictive signals for commodity markets using alternative datasets, statistical analysis, backtesting, and machine learning. Work includes sourcing and cleaning large structured and unstructured data, feature engineering, prototyping models with the Portfolio Manager, and integrating validated signals into trading strategies.

Develop and implement AI models and algorithms using Azure technologies, focusing on full-stack development, AI solutions, and collaboration with stakeholders to optimize business processes.

Own the connective layer that makes a portfolio of product agents behave as a single coherent experience. Define agent discovery, invocation, hand-off, accountability, and shared primitives (identity, context, memory, routing, guardrails). Set interface strategy across portal, APIs, MCP, and connectors; establish standards, metrics, and a multi-quarter roadmap; align cross-functional teams to ship measurable agentic journeys.

Build and ship end-to-end AI-native product features, prototype with modern AI models/APIs, collaborate with product/design, maintain fast reliable systems, and improve dev workflows and deployments.

Own strategy, roadmap, and delivery for the Sara AI product suite. Partner with engineering, customers, sales, and marketing to launch AI-driven security features, monitor model efficacy, and drive adoption while representing the product to analysts and customers.

Design, build, and ship production AI systems and platform infrastructure across distributed compute, data pipelines, backends, frontends, and observability. Develop and improve multi-agent systems, inference infrastructure, benchmarks, verification pipelines, and feedback loops to improve agent quality, cost, latency, and robustness for drug-discovery workflows.

Lead product strategy and execution for AI-native underwriting: set a 12-month vision, prototype and ship early proofs, partner with design and engineering, represent the product externally, and raise team performance to move AI from assistive to decisioning at scale.