Business Core Solutions (BCS) is an IT solutions provider specializing in automation and orchestration to streamline business operations. Their flagship product, Symphony, offers various modules including Maestro AI for business AI and intelligent automation, Replay for test automation, and BGM for background job management. BCS provides end-to-end services such as Application Managed Services, Integration, Security and Authorization, and Test Consultancy, primarily focusing on SAP and Salesforce environments. The company aims to revolutionize the IT landscape by eliminating manual processes, enhancing efficiency, and driving business growth through advanced automation technologies. They emphasize cost reduction, increased IT productivity, and enhanced compliance for their clients.
Business Core Solutions (BCS) is an IT solutions provider specializing in automation and orchestration to streamline business operations. Their flagship product, Symphony, offers various modules including Maestro AI for business AI and intelligent automation, Replay for test automation, and BGM for background job management. BCS provides end-to-end services such as Application Managed Services, Integration, Security and Authorization, and Test Consultancy, primarily focusing on SAP and Salesforce environments. The company aims to revolutionize the IT landscape by eliminating manual processes, enhancing efficiency, and driving business growth through advanced automation technologies. They emphasize cost reduction, increased IT productivity, and enhanced compliance for their clients.
We are looking for a
Senior Machine Learning Engineer
with strong foundations in classical machine learning to design, build, and deploy scalable ML solutions that power data-driven products and platforms. This role involves owning the end-to-end ML lifecycle from data ingestion and feature engineering to model training, evaluation, and production deployment while collaborating closely with data engineering, platform, and product teams.
The ideal candidate is hands-on, production-oriented, and deeply experienced in regression, classification, and large-scale data processing, with working exposure to modern Generative AI and LLM platforms as an added advantage.
Roles & Responsibilities
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Design, develop, and deploy
ML models
for regression, classification, clustering, and anomaly detection use cases
Build
end-to-end ML pipelines
including data extraction, ingestion, preprocessing, feature engineering, model training, and evaluation
Work with structured and semi-structured data to enable
scalable, high-performance ML systems
Implement
feature engineering, feature selection, normalization, and scaling techniques
for robust model performance
Deploy and monitor ML models in production using containerized and cloud-native approaches
Collaborate with data engineering teams on
ETL pipelines, data quality, and data availability
Optimize model performance, reliability, and inference latency in real-world environments
Contribute to MLOps practices including versioning, CI/CD, monitoring, and retraining workflows
Leverage
LLM-based solutions where appropriate
(e.g., text classification, enrichment, or hybrid ML + LLM pipelines)
Solid understanding of
statistics, probability, and model evaluation metrics
Proficiency in
Python
and ML libraries (scikit-learn, pandas, NumPy, XGBoost, LightGBM, etc.)
Hands-on experience with
data extraction, ingestion, preprocessing, and feature engineering
Experience deploying ML models using
Docker
and managing ML services in production
Familiarity with
MLOps practices
(model versioning, CI/CD, monitoring, retraining)
Working knowledge of
cloud ML platforms
such as Azure OpenAI, AWS SageMaker, or GCP Vertex AI
Exposure to
LLMs and Generative AI concepts
(prompting, embeddings, basic RAG) is a plus
Strong collaboration skills for working across data, platform, and product teams
Requirements
3–5 years of experience
building and deploying machine learning solutions in production environments
Proven track record of delivering
end-to-end ML projects
from data to deployment
Strong problem-solving mindset with the ability to translate business problems into ML solutions
Experience working in
cross-functional, data-driven teams
Education & Certification
Bachelor’s or Master’s degree in
Computer Science, Data Science, Machine Learning, or a related field
Relevant certifications in
ML, Data Engineering, or Cloud platforms
are preferred but not mandatory
Preferred Certifications
Machine Learning or AI certifications (AWS Certified Machine Learning – Specialty, Google Cloud Professional ML Engineer, Microsoft Azure AI Engineer Associate)
Cloud or Big Data certifications are an added advantage
