Senior Python Engineer – AI-Assisted Drug Discovery

4 weeks ago

Bengaluru, Karnataka, India Trademarkia Full time
Position: Senior Python Engineer – AI-Assisted Drug Discovery
Experience Level: 5+ years of experience in Python development, AI/ML, and cheminformatics for drug discovery applications.
Location: Bangalore, India (Hybrid) with periodic travel (2-3 times per year) to Phoenix, Arizona or Mountain View, California
Company Description
LegalForce RAPC Worldwide P.C. (parent law firm of Trademarkia) is expanding its AI and Life Sciences practice to support pharmaceutical companies and biotech startups developing cutting-edge AI-assisted drug discovery innovations. As a leader in AI-driven solutions, we are focused on developing scalable, efficient, and high-impact AI workflows that enhance the drug discovery pipeline.
With AI transforming molecular docking, cheminformatics, and predictive ADMET modeling, we are building a team of top-tier engineers to develop and optimize end-to-end AI solutions for accelerating small-molecule and biologic drug discovery.
Role Overview
We are seeking a Senior Python Engineer with expertise in AI/ML, cheminformatics, and computational drug discovery to develop scalable AI pipelines that assist medicinal chemists, pharmacologists, and data scientists in identifying and optimizing drug candidates.
Working in a hybrid capacity from Bangalore, India, you will collaborate with interdisciplinary teams and travel a few times per year to Phoenix, Arizona or Mountain View, California to engage with U.S. partners and stakeholders.
Key ResponsibilitiesAI-Driven Drug Discovery Workflows
Develop scalable Python pipelines for multi-agent AI workflows, integrating generative chemistry, virtual screening, and molecular docking.
Implement LLM-driven AI agents for cheminformatics, ADME/Tox prediction, and SAR modeling.
Utilize vector databases, embeddings, and GenAI frameworks (LlamaIndex, LangChain, CrewAI, LangGraph, AutoGen).
Cheminformatics & Molecular Modeling
Build AI-driven cheminformatics workflows using RDKit, Open Babel, AutoDock, Schrödinger, and molecular descriptor libraries.
Implement QSAR modeling, molecular fingerprinting, and predictive toxicity models.
Develop tools to automate molecular docking, lead optimization, and compound screening workflows.
MLOps & AI Infrastructure
Deploy scalable AI/ML solutions using Docker, Kubernetes, cloud-based MLOps tools, and CI/CD pipelines.
Optimize large-scale chemical and biological datasets for AI training and predictive analytics.
Automate ETL processes, data ingestion, and pre-processing for high-throughput AI modeling.
Cross-Disciplinary Collaboration
Work closely with medicinal chemists, pharmacologists, and computational biologists to refine AI models.
Stay up to date with advancements in generative AI for molecular design, deep learning in drug discovery, and computational chemistry techniques.
Collaborate with biotech R&D teams to integrate AI solutions into real-world drug discovery pipelines.
Required Technical SkillsPython & AI Development
5+ years of Python development experience with scientific and AI frameworks (PyTorch, TensorFlow, scikit-learn, NumPy, pandas).
Expertise in AI/ML for drug discovery, including generative models, predictive modeling, and molecular representation learning.
Experience with LLM frameworks and AI Agent platforms (LlamaIndex, LangChain, CrewAI, LangGraph, AutoGen).
Cheminformatics & Drug Discovery Tools
Proficiency with RDKit, Open Babel, AutoDock, Schrödinger, and molecular descriptor libraries.
Strong knowledge of QSAR modeling, pharmacokinetics (PK), and ADME/Tox predictions.
Understanding of SAR, molecular docking, and lead optimization strategies.
MLOps & Cloud Infrastructure
Experience deploying AI models on AWS, GCP, or Azure.
Expertise in Docker, Kubernetes, CI/CD pipelines, and workflow orchestration.
Proficiency in ETL, data cleansing, and automation pipelines for chemical and biological datasets.
Preferred Domain Knowledge (Chemistry & Biopharma)
Medicinal Chemistry: Understanding of drug design principles, SAR, and chemical feasibility assessments.
Pharmacology & Drug Development: Familiarity with preclinical workflows, toxicity modeling, and regulatory considerations.
Computational Chemistry: Exposure to molecular dynamics simulations (GROMACS, AMBER) or quantum chemistry calculations.
Structural Biology & Bioinformatics: Experience in protein-ligand interaction modeling, cryo-EM, crystallography, or sequence analysis.
Desired Personal Skills Collaborative Communicator : Effective at cross-disciplinary collaboration with chemists, biologists, and non-technical stakeholders.
Technical Leadership : Ability to mentor junior engineers, set engineering best practices, and influence technical direction.
Adaptability & Curiosity : Driven to stay current with emerging AI/ML methods, biopharma advances, and industry trends.
Why Join Us?
Cutting-Edge AI-Driven Drug Discovery – Work at the intersection of AI, chemistry, and biotechnology to revolutionize pharmaceutical innovation.
Impactful Work – Build AI systems that accelerate drug discovery, optimize lead compounds, and enhance predictive modeling in drug development.
Global Collaboration – Work with an exciting biotech startup and AI-driven R&D teams across India and the U.S.
Competitive Compensation – Enjoy a highly competitive salary package, plus performance-based incentives.
Career Growth & Leadership Opportunities – Be part of an expanding AI-driven drug discovery and engineering team.
How to Apply
Please submit the following via LinkedIn:
Resume/CV
Cover Letter detailing your experience in AI-driven drug discovery, cheminformatics, and Python development.
Representative Projects or GitHub Portfolio showcasing your AI/ML and cheminformatics work.
We look forward to exploring how your expertise can help shape the future of AI-assisted drug discovery at LegalForce RAPC Worldwide P.C.

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