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Senior ML Software Engineer

Job Posted 16 Days Ago Posted 16 Days Ago
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Bangalore, Bengaluru Urban, Karnataka
Senior level
Bangalore, Bengaluru Urban, Karnataka
Senior level
The Senior Machine Learning Engineer will advance LLM-powered chatbot technology, lead projects, mentor juniors, and track RAG advancements.
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What success looks like in this role:

We are seeking an experienced Senior Machine Learning Engineer to join our team. In this role, you will be pivotal in advancing our in-house LLM-powered chatbot technologies through research, development, evaluation, and analytics. You will focus on tracking and experimenting with advancements in Retrieval-Augmented Generation (RAG) and its evaluation methodologies. 
 

Key Responsibilities: 

- Define Technological Vision: Develop and articulate the technological vision for our in-house chatbot evaluation & analytics engine, ensuring alignment with company goals and industry standards. 

- Project Leadership: Collect requirements from in-house chatbot stakeholders including PMs, in-house chatbot core team, and ML team. 

- Technological Leadership: Build core chatbot’s evaluation and continuous improvements systems components. 

- Mentorship: Mentor and guide junior colleagues, fostering a culture of learning and development within the team. 

- Research Engagement: Actively participate in bi-weekly machine learning paper review meetings, staying abreast of the latest developments and integrating relevant findings into our work. 

- RAG Research Tracking: Actively track advancements in Retrieval-Augmented Generation (RAG), advanced RAG methodologies, and RAG evaluation research. 

You will be successful in this role if you have:

- Experience: Minimum of 4 years of experience leading machine learning and or data engineering projects, demonstrating a track record of successful project delivery. 

- LLM Implementation: Proven experience in implementing LLM-based solutions in production environments, with a deep understanding of the associated challenges and best practices. 

- Proficiency in Python and its machine learning libraries (e.g., Pandas, scikit-learn). 

- Experience with Spark or other distributed computation frameworks, enabling efficient processing of large datasets. 

- RAG Expertise: Strong understanding and experience with Retrieval-Augmented Generation (RAG) techniques, including tracking and experimenting with the latest advancements. 

- Mentorship: Demonstrated ability to mentor and develop other machine learning team members, sharing knowledge and expertise effectively. 

- Algorithm and Data Structures: Strong knowledge of algorithms and data structures, with the ability to apply this knowledge to solve complex problems. 

- Adaptability: Quick to grasp new technologies and concepts, staying ahead in a rapidly evolving field. 

- Communication Skills: Excellent communication skills, both written and verbal, with the ability to convey complex technical concepts to diverse audiences in English. 

Unisys is proud to be an equal opportunity employer that considers all qualified applicants without regard to age, blood type, caste, citizenship, color, disability, family medical history, family status, ethnicity, gender, gender expression, gender identity, genetic information, marital status, national origin, parental status, pregnancy, race, religion, sex, sexual orientation, transgender status, veteran status or any other category protected by law.

This commitment includes our efforts to provide for all those who seek to express interest in employment the opportunity to participate without barriers. If you are a US job seeker unable to review the job opportunities herein, or cannot otherwise complete your expression of interest, without additional assistance and would like to discuss a request for reasonable accommodation, please contact our Global Recruiting organization at GlobalRecruiting@unisys.com or alternatively Toll Free: 888-560-1782 (Prompt 4).  US job seekers can find more information about Unisys’  EEO commitment here.

Top Skills

Pandas
Python
Scikit-Learn
Spark

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