Job Description

Role: Protein Engineering Scientist (AI-Driven Large Molecule Design)

Location: Pune, Maharashtra

Work Mode: 6 days WFO

Apply here: https://forms.gle/7aj7xvsWebRvjFfi6

Role Overview

We are looking for a Protein Engineering Scientist with strong hands-on experience in computational protein design and large molecule engineering.

This role focuses on:

● protein and enzyme engineering

● antibody and binder design

● AI-driven structure and sequence generation

● end-to-end large molecule design workflows

You will work at the intersection of biology, AI, and computational modeling to design and optimize next-generation protein therapeutics and engineered biomolecules.

What You’ll Work On

● Designing and optimizing proteins, enzymes, antibodies, and other large molecules for specific functional outcomes

● Working on end-to-end protein engineering workflows from sequence to structure to function

● Applying AI-driven approaches for protein structure prediction and design

● Designing mutations to improve stability, binding affinity, specificity, and activity

● Analyzing protein-protein and protein-ligand interactions

● Integrating generative AI models into protein and enzyme engineering pipelines

● Working with evolving toolchains and adapting to new methods in AI-driven protein design

● Collaborating with cross-functional teams across computational and experimental domains

Key Responsibilities

● Execute protein engineering workflows including sequence design, structure prediction, and optimization

● Work with advanced AI-driven tools such as RFdiffusion, AlphaFold 3, ProteinMPNN, and Boltz-2

● Leverage additional emerging tools and frameworks in protein and large molecule design as required

● Design and evaluate enzyme variants and antibody candidates for therapeutic and industrial applications

● Analyze structural outputs to assess stability, folding, and binding interactions

● Develop and refine computational pipelines for large molecule and protein engineering

● Collaborate with AI/ML and backend teams to integrate protein design workflows into scalable systems

● Interpret computational outputs and translate them into actionable biological insights

Tech Stack

● Protein & Large Molecule Design: RFdiffusion, AlphaFold 3, ProteinMPNN, Boltz-2, and other emerging tools

● Molecular Modeling: PyMOL, Chimera, docking and structural analysis tools

● Simulation (Preferred): MD tools such as GROMACS, OpenMM

● Programming: Python (NumPy, Biopython, PyTorch preferred)

● Other: Sequence analysis tools, structural biology toolkits

What We’re Looking For

Core Domain Expertise

● Strong understanding of protein engineering and large molecule design workflows

● Hands-on experience in enzyme engineering, antibody design, or protein therapeutics

● Deep understanding of protein structure-function relationships

● Familiarity with stability, folding, and interaction analysis

AI-Driven Protein Design (Critical)

● Hands-on experience with modern AI-based protein design tools such as RFdiffusion, AlphaFold 3, MPNN-based methods, and related systems

● Understanding of generative approaches for protein structure and sequence design

● Ability to work with evolving AI toolchains in protein engineering

Computational & Technical Skills

● Proficiency in Python for scientific computing

● Experience with structural biology and molecular modeling tools

● Ability to work with computational pipelines and large biological datasets

Nice to Have

● Experience with molecular dynamics simulations

● Exposure to wet-lab validation workflows or experimental collaboration

● Experience in therapeutic protein or enzyme optimization pipelines

● Experience working in interdisciplinary AI + biology environments

Eligibility:

● Master’s or PhD in Biotechnology, Bioinformatics, Computational Biology, Structural Biology, or related field

● 1–5 years of hands-on experience in protein engineering / enzyme engineering / antibody design

Note: Exceptional candidates with strong project work or research experience in protein engineering and AI-driven design will also be considered.