Senior Software Engineer - Applied AI

Senior Software Engineer – Applied AI

Master’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent experience.
6+ years of experience in software development and applied ML, with a focus on building predictive models, ranking systems, and risk assessment pipelines.
Proven track record of designing, deploying, and maintaining ML and LLM models in production environments, including experience with versioning, scaling, and monitoring.
Demonstrated ability to drive measurable business impact with ML and LLM solutions — from defining success metrics to iterating based on real-world feedback.
Strong foundation in supervised, unsupervised, and reinforcement learning, as well as LLM techniques such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning.
Proficient in Python, with deep experience in libraries such as TensorFlow, PyTorch etc
Experience working with large-scale data pipelines and deploying ML models in cloud environments (AWS, GCP, or Azure).
Familiarity with model evaluation techniques, including cross-validation, A/B testing, and confidence-based ranking.
Experience building multi-agent AI systems, including listener/orchestrator agents for dynamic data ingestion and decision-making.
Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders and align teams around complex solutions.

Lead the design, development, and deployment of machine learning models for classification, recommendation, and risk assessment to enhance decision-making in drug development.
Build and optimize predictive models, including classification, regression, time-series, and ranking algorithms, with a focus on robust deployment in production environments.
Collaborate with cross-functional teams — including data scientists, engineers, and drug development stakeholders to design and deliver high-impact ML solutions.
Work with large-scale proprietary datasets (e.g., SPOKE, Evaluate data, KOL transcripts), ensuring performance benchmarks are met within cloud-based infrastructure (AWS, GCP, Azure).
Rapidly prototype and iterate on machine learning solutions, including proofs-of-concept, to explore innovative approaches and evaluate business impact.
Integrate structured knowledge into ML pipelines using ontologies and document store architectures, enhancing model context and data retrieval.
Design and build AI-driven listener agents that continuously ingest new information and trigger reanalysis of drug candidates using updated predictive models.
Stay current with emerging trends in machine learning and generative AI, applying them to continuously evolve our modeling and decision-making capabilities.

Experience in the pharmaceutical or biotech industry is a plus, but not required.