Artificial Intelligence & Engineering
AI & Engineering leverages cutting-edge engineering capabilities to help build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These insights are powered by engineering for business advantage, helping transform mission-critical operations.
Join our AI & Engineering team to help transform technology platforms, driving innovation, and help make a significant impact on our clients' achievements. Youll work alongside talented professionals reimagining and re-engineering operations and processes that could be critical to businesses.
Position Summary
Level:
As a Sr. Engineer 2 at Deloitte Consulting, you will design, develop, and deploy enterprise-scale software solutions, lead the creation of robust pipelines and manage code deployment across environments. You will collaborate with cross-functional, global teams to translate functional requirements into effective deliverables, independently guiding and mentoring junior team members. Your role spans the full project lifecycle, including estimation, planning, execution, and tracking key metrics for analysis, ensuring high-quality and timely delivery of solutions.
Work you’ll do:
Lead discussions with business and functional analysts to understand requirements and assess integration impacts on business architecture. Prepare technical architecture and design, clarify requirements, and resolve ambiguities. Develop solutions in line with established technical designs, integration standards, and quality processes. Create and enforce technical design and development guides, templates, and standards. Facilitate daily scrum meetings, manage deliverables, and prepare weekly status reports for leadership review. Conduct detailed deliverable reviews and provide technical guidance to team members. Collaborate with onsite clients, coordinators, analysts, and cross-functional teams. Design templates or scripts to automate routine development or operational tasks.
- Strategize and enable end-to-end journey from on-premises to cloud or building cloud native businesses
- Own and manage specific work threads of a larger consulting engagement; participate in interviews, workshops, training sessions, and support team and client
- Lead articulation of problem statement, development of storyboard and own deliverables
- Collect and analyze primary / secondary research data, draw conclusions, and support development of strategic insights or scenario specific business cases and models
- Perform Cloud Migration and PMO for large scale transformations and revisit client businesses and operating models to unearth new opportunities
- Lead critical practice initiatives and assist managers in developing proposals
- Technical Expertise in Python / Scala and Java.
- Strong proficiency in Python/Scala programming with extensive experience in designing, developing, and deploying scalable applications and engineering solutions.
- Ability to write clean, maintainable, and reusable code adhering to industry best practices.
- Proficient in architecture patterns such as Layered (N-Tier) ETL, Microservices, Event Driven Serverless, Data Pipeline etc.
- Cloud Computing (AWS/Azure):
- Deep expertise in cloud platforms such as AWS (S3, EMR, Lambda, Glue, Redshift, Athena, Kinesis, DynamoDB, Step Functions, ECS, CloudFormation) or Azure (Data Factory, Databricks, Synapse Analytics, Data Lake, Event Hubs, Blob Storage, Azure Functions).
- Proficiency in leveraging cloud-native services for data storage, ETL pipelines, real-time streaming, and orchestration.
- Big Data and Spark Expertise:
- Experience working with Apache Spark and cloud-native Spark solutions like AWS EMR, Azure Databricks, or Google Dataproc.
- Expertise in optimizing Spark jobs for performance and managing large-scale data processing workflows.
- Orchestration and Workflow Automation:
- Hands-on experience with orchestration tools such as Apache Airflow, AWS Step Functions, Azure Data Factory Pipelines, or Prefect for managing complex data workflows.
- Data Engineering Proficiency:
- In-depth knowledge of data engineering tools and technologies, such as:
- ETL tools: Glue, Azure Data Factory
- Data streaming: Kafka, AWS Kinesis, Azure Event Hubs.
- Data storage and querying: Snowflake, Redshift, BigQuery, Synapse Analytics, Athena, Delta Lake.
- Database systems: PostgreSQL, MySQL, Cassandra, MongoDB.
- Performance Optimization:
- Proven ability to optimize Spark jobs, SQL queries, and ETL workflows.
- Experience in identifying and resolving performance bottlenecks in distributed data systems, cloud-based solutions, and pipelines.
- Code Reviews and Best Practices:
- Expertise in conducting code reviews to ensure adherence to SOLID principles, clean architecture, and industry-standard best practices.
- Ability to mentor and guide junior developers in improving code quality and maintainability.
- System Architecture and Design:
- Strong experience in architecting scalable, cost-effective, and resilient systems using technologies like AWS Glue, Azure Synapse, Lake Formation, and Databricks.
- Proficiency in designing and implementing Data Lakes, Data Warehouses, and real-time data pipelines.
- Collaboration and Communication:
- Excellent communication skills to effectively collaborate with cross-functional teams, including data scientists, DevOps, and stakeholders.
- Ability to lead technical discussions and influence architectural decisions.
- Continuous Learning and Innovation:
- Commitment to staying updated with the latest advancements in Python, cloud computing, and data engineering technologies.
- Experience with emerging tools such as Terraform for infrastructure as code, and Delta Lake for managing data pipelines.
- Capability to evaluate and integrate cutting-edge tools and technologies for continuous improvement.
- Develop, test, and refine prompts to elicit desired responses from AI models.
- Experiment with prompt structures, context windows, and input formatting to improve output quality.
- Test and compare different models and APIs to select the best fit for specific tasks.
The team:
Engineering as a Service provides complete design, implementation, and technology operations, leveraging our core engineering expertise. We transform engineering teams, modernize technology, and deliver complex programs with a product engineering approach. Our flexible delivery models—traditional teams, pools, or pods—are tailored to each client’s needs, offering engineering-led advisory, implementation, and operational capabilities to accelerate innovation.
Qualifications
Must Have Skills/Project Experience/Certifications:
- 6 – 10 years of project experience on Cloud Suitability Analysis, Application Rationalization, Business Case preparation, Cloud Operating Model, Cloud Resiliency, Cloud FinOps, Program Management Office, Data Center Consolidation and/or exit
- Knowledge of major Infrastructure-as-a-service (IaaS) providers
- Deep understanding of cloud providers including services, licensing model, technology stack and pricing model
- Experience in client interaction on a regular basis to understand their processes and business problems
- Experience in leading Project Teams with good interpersonal and communication skills
- Ability to perform solid storyboarding and conduct financial model analysis
- Knowledge and experience working with Microsoft Office tools
- 10+ years of industry experience, with at least 6+ years in a technical architect role designing applications and hosting solutions as public/private cloud SaaS PaaS platforms.
- At least 5+ years of hands-on experience in cloud native architecture design, implementation of distributed, fault-tolerant enterprise applications for cloud.
- Experience in application migration and modernization of legacy applications to AWS cloud platforms
- Familiarity of principles summarized in the AWS Cloud Adoption and Well-Architected Frameworks
- Deep expertise of architecting and designing end-to-end AWS solutions architecture, recommending best cloud architecture patterns suiting the business problems, (Integrating services like: Glue, Redshift, EMR, Step Function, Amazon MQ, Lambda, AWS S3 and storage classes, etc.)
- Working experience of designing approach and migrating solution from on-prem using AWS Database Migration, Migration Hub, Application Migration Service, etc.
- Good to have hands-on experience with Kubernetes, EKS or OpenShift container platforms
- Strong understanding of BigData architecture, design and implementation creating cloud architecture leveraging PaaS.
- Strong understanding of the cloud native development practices and architectural patterns.
- Understanding of data services including RDS, DynamoDB, Amazon Keyspace, DocumentDB, ElastiCache, RedShift and when to use each.
- Understanding of the impact of technology on client business processes and plan the risk mitigation steps.
- Experience in leading and managing teams within specialist areas from project planning to execution
- Stay current with advancements in generative AI, LLMs, and prompt engineering techniques.
- Candidates with AWS Solution architect certifications will be preferred (SAA-C03)
Education:
- BE/B. Tech/M.C.A./M.Sc. (CS) degree or equivalent from accredited university.
Location:
