Job Title: ML Engineer

Experience: 2-4Years

Location: Delhi/Bangalore (Hybrid)

Role Overview:

As an ML Engineer at Elucidata, you will be responsible for bridging the gap between cutting-edge research in biomedical data science and real-world production deployments. You will collaborate with a team of data scientists, bioinformaticians, and software engineers to build, deploy, and maintain robust ML/AI pipelines for a wide range of applications—from natural language processing (NLP) and computer vision (CV), to foundational model development in single-cell RNA-seq and spatial transcriptomics. Your work will ensure our models are scalable, reliable, and deliver meaningful impact to our customers and scientific community.

Key Responsibilities:

• Productionize ML models: Transition prototypes into scalable, reliable solutions.
• Build ML pipelines: Design end-to-end workflows (data ingestion → deployment) with MLOps best practices.
• Optimize performance: Ensure low-latency, high-throughput inference for large-scale biomedical datasets.
• Deploy & monitor: Implement APIs, model versioning, drift detection, and cloud/on-prem solutions (AWS/GCP/Azure).
• Cross-functional collaboration: Partner with research, product, and compliance teams to deliver impactful solutions.

Required Skills & Experience:

• Core: Python, PyTorch/TensorFlow, MLOps (MLflow, Airflow), cloud (AWS/GCP/Azure), distributed computing (Spark/Ray).
• Bonus: Genomics/bioinformatics knowledge, CI/CD for ML (Kubeflow), model optimization (quantization/pruning).
• Soft Skills: Strong communication, problem-solving, and teamwork in interdisciplinary environments.

Qualifications

Must-have:

• 3+ years of ML engineering experience, including production deployment of models.
• Proficiency in Python, PyTorch/TensorFlow, MLOps tools (MLflow, Kubeflow), and cloud platforms (AWS/Azure/GCP).
• Hands-on experience with scaling DL models (GPU/TPU clusters) and optimizing for performance (latency, throughput).
• Familiarity with CI/CD for ML (e.g., Kubeflow, Tekton) and distributed computing (Spark/Ray).

Nice-to-have:

• Background in biology, genomics, or bioinformatics (to contextualize biomedical data).
• Experience with model optimization (quantization, pruning) and open-source contributions (ML/bioinformatics tools).
• Knowledge of software development best practices (Agile, APIs, microservices).