Description : Title : ML Type : Full time, : : BE/B.tech/MCA Degree in Computer Science, Engineering, or similar relevant Experience : 4+ Model : Work from the Role :
We are seeking a skilled Machine Learning Engineer to design, build, and deploy scalable, data-driven solutions for enterprise environments.
Responsibilities
The ideal candidate will collaborate closely with clients and cross-functional teams to develop ML systems, optimize performance, and deliver measurable business Responsibilities :
- Collaborate with clients, data scientists, and engineering teams to deliver end-to-end machine learning solutions.
- Identify opportunities to implement new ML techniques and build pipelines to enable enterprise-scale deployment.
- Translate client requirements into data-driven models, processes, and analytical metrics.
- Analyze and transform large datasets across cloud platforms such as AWS, Azure, and GCP.
- Design, develop, and deploy advanced analytics solutions (e.g., recommender systems, NLP models, risk scoring).
- Productionize ML systems with a strong focus on optimization, scalability, and reliability.
- Contribute to practice growth through client presentations, proposal writing, business development activities, and mentoring junior team :
- Bachelors degree or equivalent experience.
- Hands-on experience handling and analyzing large (multi-billion-record) datasets.
- Strong programming skills in Python, Scala, or similar languages.
- Proficiency in SQL, MapReduce/Hive, NoSQL, and modern distributed data-processing frameworks.
- Working knowledge of data warehousing tools/environments such as Snowflake, Teradata, SQL Server, RDS, and Presto.
- Ability to frame data-driven business problems across industries.
- Experience in analytical domains such as consumer, marketing, financial, website, healthcare, or social media analytics.
- Hands-on expertise in implementing production-scale ML systems (NLP, personalization, computer vision, etc.)
- Understanding of DevOps and automation practices.
- Strong knowledge of statistics, modeling techniques, and model management/versioning.
(ref:hirist.tech)
IN
