Snowflake Cortex AI Engineer

Job Description: Python + AI

Company: Wissen Technology

Location: Bangalore, India

Experience: 5+Years

Employment Type: Full-Time

Role: Python + AI / Data Engineer

About the Role

Wissen Technology is looking for experienced Python + AI / Data Engineering professionals to join our technology team in Bangalore. The ideal candidate will have strong hands-on experience in Python, Artificial Intelligence, Generative AI, PySpark, Snowflake, and data pipeline development.

The candidate should be capable of designing and developing scalable data and AI solutions, building robust ETL/ELT pipelines, working with large datasets, and integrating AI/ML capabilities into enterprise applications.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Python and PySpark.
• Develop robust ETL/ELT pipelines for processing large volumes of structured and unstructured data.
• Build and optimize data processing solutions using Apache Spark / PySpark.
• Develop data ingestion and transformation pipelines into Snowflake.
• Design and implement scalable Snowflake data models, tables, views, and SQL transformations.
• Work with batch and, where applicable, real-time data processing pipelines.
• Build and integrate AI and Generative AI solutions using Python.
• Develop LLM-based applications, RAG solutions, AI agents, and AI-powered services.
• Integrate AI models with enterprise data platforms and data pipelines.
• Develop REST APIs and microservices using FastAPI, Flask, or Django.
• Perform data cleansing, transformation, validation, and quality checks.
• Optimize PySpark jobs, SQL queries, Snowflake workloads, and data pipelines for performance and scalability.
• Implement data pipeline monitoring, logging, error handling, and alerting.
• Work with cloud platforms such as AWS, Azure, or GCP.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, Architects, and business stakeholders.
• Participate in technical design, architecture, code reviews, and production support.
• Mentor junior engineers and contribute to engineering best practices.

Preferred Qualifications

• Bachelor's or master's degree in computer science, Engineering, Data Science, Artificial Intelligence, or a related field.
• Experience working on enterprise-scale AI and data engineering projects.
• Experience combining Python + PySpark + Snowflake + AI/GenAI in production environments.
• Experience with Databricks is an advantage.
• Experience with AI Agents / Agentic AI and tool/function calling.
• Knowledge of distributed systems and cloud-native architecture.
• Experience leading technical initiatives or mentoring engineering teams.

About the Role

You'll be at the forefront of designing and implementing robust data platform solutions that power advanced analytics, AI, and machine learning. Working with modern cloud technologies, you'll build scalable data foundations that enable clients to make smarter, data-driven decisions.

Key Responsibilities

• Build scalable data pipelines using Snowflake, AWS, GCP, and Databricks.
• Design and optimize data models for AI and machine learning workloads.
• Develop reliable data foundations for MLOps, governance, and data lineage.
• Integrate data from multiple sources into modern data platforms.
• Leverage Snowpark ML and Snowflake's native AI capabilities.
• Ensure data platforms are secure, scalable, and high-performing.

What We're Looking For

• 5+ years of hands-on experience with Snowflake.
• Strong proficiency in SQL and Python.
• Experience with AWS, Azure, or GCP.
• Knowledge of cloud storage services such as S3, ADLS, or GCS.
• Strong understanding of Dimensional Modeling and Data Vault.
• Experience with Scala or Java is a plus.

Tech Stack

• Data Warehouse: Snowflake
• Programming: SQL, Python, Scala (Good to Have), Java (Good to Have)
• Cloud: AWS, Azure, GCP
• Storage: S3, ADLS, GCS
• AI/ML: Snowpark ML, MLOps

Perks & Benefits

• Public Speaking & Communication Program
• Mentoring Program with Senior Support Leads
• 360° Progress Reviews
• Weekly Learning Sessions & Guilds
• Paid Certifications
• Hackathons & Innovation Days
• Recognition & Rewards Programs
• Team Socials & Annual Offsites
• Employee Assistance Program (24/7 Wellbeing Support)

The Data People Shaping Tomorrow

Our client helps organizations unlock the power of data through modern cloud, analytics, and AI solutions. We believe in creating an environment where talented technologists can learn, innovate, and make a real impact while building cutting-edge data platforms for global clients. If you're passionate about data engineering and want to work with the latest technologies in AI, cloud, and analytics, we'd love to hear from you.

Role Summary:

We are looking for an experienced Snowflake Lead to lead the design, development, migration, and optimization of enterprise data platforms using Snowflake. The candidate will provide technical leadership to data engineering teams and work closely with architects, business stakeholders, and application teams.

Key Responsibilities

• Lead the architecture and development of scalable Snowflake data warehouse solutions.
• Design and develop scalable Azure Data Factory (ADF) pipelines for API-based and batch data ingestion, implementing parameterized workflows, scheduling, and error handling.
• Build and optimize enterprise Snowflake data warehouse solutions using Snowflake SQL, Streams, Tasks, Stored Procedures, VARIANT data type, and LATERAL FLATTEN for semi-structured JSON processing.
• Integrate GraphQL and REST APIs using OAuth 2.0, implementing secure API authentication, JSON parsing, and API validation using Postman.
• Develop cloud-based data ingestion solutions using Azure Data Lake Storage Gen2 (ADLS) as the landing layer and Azure Key Vault for secure credential management.
• Design metadata-driven ELT frameworks with incremental loading, audit logging, watermark processing, and automated data orchestration.
• Optimize Snowflake performance through warehouse sizing, query tuning, clustering strategies, Time Travel, Cloning, and warehouse management best practices.
• Collaborate with DevOps teams using Azure DevOps for source control, CI/CD deployment, release management, and Agile delivery.
• Design dimensional data models, build curated data marts, and support enterprise reporting and analytics requirements.
• Develop and maintain Power BI semantic models, datasets, dashboards, and reports; knowledge of DAX, Power Query, and data visualization best practices is preferred.
• Work closely with business stakeholders, solution architects, and cross-functional teams to deliver secure, scalable, and high-performance cloud data platform solutions.

Thanks,

Mounika P

Experience: 4+ Years 

Location: India (Bangalore, Hyderabad/ Mumbai/ Gurugram)

Role Summary:

AuxoAI is seeking a Senior GenAI Data Engineer with strong fundamentals in data engineering and end-to-end solution design. In this role, you will design and develop production-grade pipelines, leverage GenAI tools (Copilot, Claude, Gemini) to boost development productivity, and define engineering best practices across complex data environments. This is a highly collaborative, cross-functional role — ideal for someone who thrives at the intersection of data engineering excellence and GenAI-powered innovation.

Responsibilities:

• Architect and develop end-to-end data pipelines — from ingestion to transformation to consumption

• Lead solutioning and integration for complex data workflows (batch and streaming)

• Use AI-assisted coding tools (e.g., GitHub Copilot, Claude, Gemini) to accelerate code development, refactoring, and debugging

• Implement robust data quality, testing, lineage, and governance frameworks

• Drive best practices across pipeline performance, reusability, and scalability

• Mentor junior engineers and contribute to capability building within the data team

Requirement:

• 4+ years of experience in data engineering, with expertise in:

      o End-to-end pipeline development (batch and streaming)

      o Data modeling (dimensional, Data Vault, OBT)

      o ETL/ELT design patterns, performance tuning, and optimization

      o SQL (Advanced) and Python (Advanced)

      o Apache Spark for large-scale data processing

• Proficiency using AI coding tools (e.g., Copilot, Claude, Gemini) to enhance productivity and code quality

• Strong understanding of data quality frameworks, unit testing, and CI/CD for data workflows

• Experience with Google Cloud Platform services: o BigQuery, Dataflow, Cloud Composer, Pub/Sub, Dataproc, Vertex AI

• Exposure to finance or sales data domains

• Familiarity with Databricks, Delta Lake, or Apache Iceberg

• GCP Professional Data Engineer certification is a plus

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 30 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Senior Data Engineer – Ab Initio | GCP | Spark | Agentic AI

Location: Bangalore

Experience: 5+ Years

Role: Senior Data Engineer

Work Mode: Bangalore

Job Summary

We are looking for an experienced Senior Data Engineer with strong expertise in Ab Initio, GCP, Apache Spark, and Agentic AI. The ideal candidate will have hands-on experience designing and developing scalable data engineering solutions, building data pipelines, and working with modern cloud and AI technologies.

The candidate should be comfortable working across traditional enterprise data platforms and emerging Generative AI / Agentic AI solutions.

Key Responsibilities

• Design, develop, and maintain scalable and high-performance data pipelines using Ab Initio, Spark, and GCP services.
• Develop and optimize complex ETL/ELT workflows using Ab Initio.
• Build and maintain data processing solutions using Apache Spark / PySpark.
• Develop cloud-based data solutions on Google Cloud Platform (GCP).
• Work with GCP data services such as BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, or equivalent services.
• Perform data integration, transformation, cleansing, and validation.
• Optimize data pipelines for performance, scalability, reliability, and cost.
• Collaborate with data architects,
• 
  

Job Title : Data Engineer – Databricks

Experience : 6+ Years

Location : Noida / Hyderabad / Chennai / Pune / Bengaluru (Hybrid)

Shift : IST (Normal Shift)

Job Summary :

We are seeking an experienced Data Engineer with strong expertise in Databricks, Snowflake, Python, and Spark to build and optimize scalable data pipelines and support AI/ML model deployments. The ideal candidate should have experience working with cloud-based data platforms and preferably possess exposure to the Healthcare domain.

Required Skills :

• Databricks (Preferred)
• Snowflake
• Python
• Apache Spark
• SQL
• Azure Cloud
• Kubernetes
• Apache Airflow
• GitHub & CI/CD Pipelines
• AI/ML Model Deployment
• Data Analytics

Preferred :

• Experience in the Healthcare domain.
• Strong understanding of scalable data engineering architectures and best practices.

About the Role

We are looking for a Senior Data Engineer with strong hands-on expertise in Databricks, Python, PySpark, and SQL to build scalable, high-performance data engineering solutions. You’ll architect and develop large scale, high-performance data pipelines capable of handling massive real-time and batch data volumes across multiple business systems. Databricks is the core enterprise data and processing platform for this role. You will also use Apache Airflow for workflow orchestration and dbt for ELT transformations, and will contribute to designing reliable, secure, and governed data platforms that enable analytics, reporting, and AI-driven use cases.

Key Responsibilities

• Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
• Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
• Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
• Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
• Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
• Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.

Required Skills

• Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
• Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
• Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
• Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
• Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
• Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
• Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
• Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
• Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
• Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.

Nice to Have

• Hands-on exposure to Microsoft Fabric for data integration and analytics.
• Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
• Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
• Domain expertise in financial services.

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 30 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.