Machine Learning Engineer

Job Description

<p>We’re looking for a Machine Learning Engineer to join our Applied AI Lab and play a hands-on role in building intelligent, scalable, and production-ready ML systems. You’ll work at the intersection of rapid prototyping and product-focused ML, contributing to both short-cycle innovation and long-term platform stability.</p><p>If you enjoy wearing multiple hats, thrive in fast-paced environments, and are excited about shaping how AI transforms healthcare and life sciences, this opportunity is for you.</p><h3>Key Responsibilities</h3><ul><li>Solve business problems with cutting-edge AI solutions, focusing on cost-efficiency and reliability.</li><li>Collaborate with Lab leads, product designers, researchers, and architects to scope MVPs and define core capabilities.</li><li>Build modular, reusable components for GenAI solutions and traditional ML pipelines (training, evaluation, deployment).</li><li>Design and develop API-based AI tools and batch data transformation workflows.</li><li>Implement end-to-end solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance evaluation.</li><li>Develop production-grade solutions following software engineering best practices.</li><li>Support experimentation, A/B testing, and fine-tuning cycles.</li><li>Stay updated on the latest advancements in Generative AI and related technologies.</li><li>Document architectures, processes, and technical decisions.</li></ul><h3>Qualifications</h3><ul><li>Bachelor’s/Master’s in Computer Science, Engineering, Data Science, or related field.</li><li>4.5+ years of experience as an AI/ML Engineer.</li><li>Strong knowledge of Python, SQL, and data processing languages.</li><li>Solid understanding of ML concepts (GenAI, NLP, LLMs).</li><li>Hands-on experience with open-source LLMs.</li><li>Proficiency in software engineering fundamentals (unit testing, modular design, CI/CD).</li><li>Strong experience with ML/AI frameworks (scikit-learn, PyTorch, TensorFlow, HuggingFace).</li><li>Familiarity with cloud platforms (AWS/Azure), containerization (Docker), and version control (Git).</li><li>Exposure to ETL tools (Spark, Airflow), PySpark, data modeling/warehousing, and CI/CD pipelines is a plus.</li><li>Experience with vector embeddings, multimodal data, agentic workflows, and LLM fine-tuning/RAG pipelines.</li><li>Exposure to healthcare/life sciences datasets (EHR, claims, clinical trials).</li><li>Prior experience in startup or R&D environments preferred.</li><li>Excellent communication and collaboration skills.</li></ul>

We’re looking for a Machine Learning Engineer to join our Applied AI Lab and play a hands-on role in building intelligent, scalable, and production-ready ML systems. You’ll work at the intersection of rapid prototyping and product-focused ML, contributing to both short-cycle innovation and long-term platform stability.

If you enjoy wearing multiple hats, thrive in fast-paced environments, and are excited about shaping how AI transforms healthcare and life sciences, this opportunity is for you.

Key Responsibilities

Solve business problems with cutting-edge AI solutions, focusing on cost-efficiency and reliability.
Collaborate with Lab leads, product designers, researchers, and architects to scope MVPs and define core capabilities.
Build modular, reusable components for GenAI solutions and traditional ML pipelines (training, evaluation, deployment).
Design and develop API-based AI tools and batch data transformation workflows.
Implement end-to-end solutions for batch and real-time algorithms, including monitoring, logging, automated testing, and performance evaluation.
Develop production-grade solutions following software engineering best practices.
Support experimentation, A/B testing, and fine-tuning cycles.
Stay updated on the latest advancements in Generative AI and related technologies.
Document architectures, processes, and technical decisions.

Qualifications

Bachelor’s/Master’s in Computer Science, Engineering, Data Science, or related field.
4.5+ years of experience as an AI/ML Engineer.
Strong knowledge of Python, SQL, and data processing languages.
Solid understanding of ML concepts (GenAI, NLP, LLMs).
Hands-on experience with open-source LLMs.
Proficiency in software engineering fundamentals (unit testing, modular design, CI/CD).
Strong experience with ML/AI frameworks (scikit-learn, PyTorch, TensorFlow, HuggingFace).
Familiarity with cloud platforms (AWS/Azure), containerization (Docker), and version control (Git).
Exposure to ETL tools (Spark, Airflow), PySpark, data modeling/warehousing, and CI/CD pipelines is a plus.
Experience with vector embeddings, multimodal data, agentic workflows, and LLM fine-tuning/RAG pipelines.
Exposure to healthcare/life sciences datasets (EHR, claims, clinical trials).
Prior experience in startup or R&D environments preferred.
Excellent communication and collaboration skills.

TalentAQ

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Job Description

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