Data Science & Engineering Lead at Jobgether

, , India -

Full Time

Start Date

Immediate

Expiry Date

04 Sep, 26

Salary

0.0

Posted On

06 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Engineering, MLOps, Deep Learning, NLP, Computer Vision, Apache Spark, AWS, Azure, Databricks, Lakehouse Architecture, ETL/ELT, Python, SQL, Distributed Computing, Leadership

Industry

Internet Marketplace Platforms

Description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Science & Engineering Lead based in India. This is a senior, hands-on leadership role at the intersection of data science, machine learning, and data engineering, focused on building scalable AI-driven systems that power real-world business outcomes. You will lead the design and deployment of advanced ML models across supervised, unsupervised, deep learning, NLP, and computer vision domains while also owning the underlying data architecture that enables them. The role involves working across modern cloud and big data ecosystems such as AWS or Azure, Databricks, Spark, and lakehouse architectures. You will play a key role in shaping end-to-end ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring. Beyond technical execution, you will mentor engineers, drive best practices in MLOps, and influence architectural decisions at scale. The environment is fast-paced, innovation-driven, and highly collaborative, with a strong emphasis on building production-grade AI systems that deliver measurable impact. \n Accountabilities: Lead the design, development, and deployment of advanced machine learning models across supervised, unsupervised, deep learning, NLP, and computer vision use cases. Architect and optimize scalable data pipelines for batch and streaming data processing using modern frameworks and tools. Build and maintain lakehouse architectures (Delta/Iceberg) and implement bronze-silver-gold data modeling standards. Design and manage ETL/ELT workflows using tools such as Airflow, DBT, and Airbyte to ensure reliable data transformation and movement. Lead MLOps initiatives, including model deployment, monitoring, and lifecycle management using platforms like Databricks, SageMaker, or Azure ML. Develop and optimize distributed data processing and training workflows using Spark, EMR, Glue, and similar big data technologies. Oversee database design and optimization across OLTP and OLAP systems, including relational and NoSQL databases. Drive cloud architecture decisions and implementations on AWS or Azure, including infrastructure components and data services. Build BI and analytics solutions using tools like Power BI, Tableau, or QuickSight to deliver actionable business insights. Mentor and guide junior engineers, promoting best practices in AI/ML engineering, data architecture, and software development. Requirements: 7+ years of experience in data science, machine learning, or data engineering roles with proven leadership responsibilities. Strong expertise in supervised and unsupervised learning, deep learning, and neural network architectures (CNNs, RNNs, GANs, Transformers). Hands-on experience with ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. Proven experience building and deploying production-grade ML systems using MLOps platforms (Databricks, SageMaker, Azure ML). Strong knowledge of big data processing frameworks such as Apache Spark, EMR, and AWS Glue. Expertise in designing ETL pipelines and data workflows using Airflow, DBT, and Airbyte. Solid understanding of lakehouse architecture (Delta, Iceberg) and data quality frameworks. Strong proficiency in cloud platforms (AWS or Azure), including core services such as IAM, networking, Lambda, Kafka, and API Gateway. Experience with SQL and NoSQL databases, with strong skills in data modeling for OLTP and OLAP systems. Familiarity with LLM ecosystems and AI tooling such as LangChain, Hugging Face, or OpenAI APIs is highly desirable. Strong leadership and mentoring skills with the ability to guide cross-functional technical teams. Excellent communication skills and ability to collaborate with stakeholders to deliver high-impact solutions. Bachelor’s degree in a relevant field (Master’s or PhD preferred). AWS or Azure certifications are a plus. Benefits: Competitive compensation aligned with senior data and AI leadership roles. Opportunity to lead cutting-edge AI, ML, and data engineering initiatives at scale. Exposure to modern cloud, big data, and MLOps ecosystems in production environments. Flexible work arrangements depending on project and team structure. Strong focus on innovation, technical growth, and continuous learning. Collaborative, engineering-driven culture emphasizing ownership and impact. Work on high-impact data products that directly influence business decisions. \n How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1

Responsibilities

Lead the design and deployment of advanced ML models and scalable data architectures to drive business outcomes. Oversee end-to-end ML pipelines, MLOps initiatives, and mentor junior engineers in a collaborative environment.