Work setup in the employer’s posting: hybridFrom the employer’s posting
Employment type Full-time
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Design and implement AI/ML pipelines for life sciences problems like territory alignment and patient segmentation
Build and deploy scalable AI services and LLM-based copilots for field force and commercial teams
Lead data ingestion, feature engineering, and model development on structured and unstructured data
Implement GenAI and Agentic AI frameworks for commercial automation
Conduct technical reviews and mentor junior AI team members
Design and implement AI/ML pipelines that solve real-world problems in life sciences (e.g., territory alignment, rep scheduling, patient segmentation, incentive compensation optimization).
Build and deploy scalable AI services and LLM-based copilots (e.g., for field force, MSLs, or commercial analytics teams).
Lead efforts in data ingestion, feature engineering, and model development across structured and unstructured data sources
Implement GenAI and Agentic AI frameworks for commercial automation.
Conduct technical reviews and provide mentoring to junior AI team members.
- Excellent communication skills with the ability to explain complex technical ideas to non-technical stakeholders. - Bachelor's/Master's in Computer Science, Data Science, Engineering, or related field. At Beghou Consulting, you'll join a highly collaborative, values-driven team where technical excellence, analytical rigor, and personal growth converge. Whether you're passionate about AI innovation, building commercialization strategies, or shaping the next generation of data-first solutions in life sciences, this is a place to make an impact!
Open 6 monthsWe first saw this posting 191 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 191
Up longer than most. Similar jobs usually come down between day 8 and day 76.
Well past Beghouconsulting’s usual: its jobs come down by about day 58.
From 5,460 similar postings that came down, and 45 of Beghouconsulting’s. Coming down is not always a hire.
22 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- At least 7 years of experience in AI/ML engineering. - Proficient in Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow, LangChain, Hugging Face). - Experience building and deploying machine learning models in production.
- Conduct technical reviews and provide mentoring to junior AI team members. - Collaborate with DevOps and cloud teams to deploy models on AWS/Azure. - Present technical concepts and outcomes to internal stakeholders and client teams.
- Exposure to LLMs, transformers, and retrieval-augmented generation (RAG) techniques. - Experience working with cloud environments (AWS, Azure preferred, Databricks). - Excellent communication skills with the ability to explain complex technical ideas to non-technical stakeholders.
- Experience building and deploying machine learning models in production. - Strong understanding of MLOps tools and practices (e.g., MLflow, Docker, Git, CI/CD). - Exposure to LLMs, transformers, and retrieval-augmented generation (RAG) techniques.
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New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Full-time
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Bangalore
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