Quantiphi

As a Senior Machine Learning Engineer at Quantiphi, you will be responsible for designing and developing advanced machine learning models and algorithms to solve complex business problems. You will work on optimizing and deploying these models on AWS infrastructure, ensuring scalability and reliability.

Must have skills:

● Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or similar Agentic AI frameworks.

● Python: Good exposure to Python (Pandas, NumPy, FastAPI, advanced Python concepts).

● AWS Bedrock: Hands-on experience with AWS Bedrock and foundation models such as Claude Haiku and Claude Sonnet.

● LLM & GenAI: Hands-on experience in developing RAG pipelines, Prompt Engineering, and LLM-based GenAI applications.

● ML Pipeline Architecture: Experience with Titan Embeddings, Amazon OpenSearch Vector Search, and vector-based retrieval.

● Agentic AI: Hands-on experience with Agentic AI frameworks and AWS Bedrock AgentCore for developing AI agents and workflow orchestration.

● AI Agents & Knowledge Base: Experience developing AI agents for customer query automation and Knowledge Base (KB) solutions.

● Experience with document parsing, chunking, vectorizing and re-ranking strategies.

● Guardrails & Security: Experience configuring AWS Bedrock Guardrails and implementing authentication and authorization for secure AI applications.

● AWS Services: Hands-on experience with API Gateway, Lambda, S3, IAM, CloudWatch, ECR, and SageMaker.

● Software Engineering: Experience with Git, REST APIs, and CI/CD pipelines.

● Relevant AWS certifications (e.g., AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer – Associate, or AWS Certified Solutions Architect – Associate) are a plus.

Good to have skills:

● Hands-on experience with OCR/Document Intelligence engines and NLP techniques for extracting structured information from documents and images.

● Hands-on experience with Bedrock Agentcore

● Hands-on experience with OpenAI, Anthropic, Gemini, or other foundation models.

● Experience with Docker, Kubernetes, and MLOps practices.

● Experience with Redshift, SQL, DynamoDB, or AWS Glue.

● Exposure to model monitoring, evaluation, and observability for GenAI applications.

● Ability to work in an Agile and DevOps environment.

As an AWS Platform Engineer – Security Specialist, you will be responsible for designing, building, and securing AWS platform infrastructure using Infrastructure as Code (IaC). You will implement cloud security controls, automate compliance, secure CI/CD pipelines, enable centralized monitoring, and collaborate with engineering teams to build highly secure cloud-native platforms.

Key Responsibilities

• Design, build, and secure AWS platform infrastructure using CloudFormation and Terraform.
• Implement and manage AWS security controls including IAM, KMS, Secrets Manager, and AWS Network Firewall.
• Build automated security guardrails and compliance checks using AWS Security Hub, AWS Config, and IAM Access Analyzer.
• Develop secure CI/CD pipelines with automated policy validation, vulnerability scanning, and artifact integrity checks.
• Implement centralized logging and monitoring using CloudWatch, GuardDuty, SIEM tools, and VPC Flow Logs.
• Collaborate with Application and DevOps teams to define secure architecture patterns, network segmentation, and Zero Trust controls.
• Conduct security assessments, risk reviews, and threat modeling for AWS workloads.
• Enforce tagging standards, data classification, and lifecycle policies across AWS resources.
• Support incident response, root cause analysis, remediation planning, and post-incident improvements.
• Prepare security documentation, runbooks, and platform security best practices.
• Integrate and manage security tools including SIEM, DLP, CASB, Cloud Proxy, and Isolation solutions.
• Provide guidance on AWS security, identity governance, and least-privilege access.
• Integrate AWS workloads with Security Operations Center (SOC) processes for real-time threat detection and incident response.

Must Have Skills

AWS Security

• 7+ years of experience in AWS Cloud Platform / Security Engineering.
• 3–5 years of hands-on experience in Cloud Security / Cybersecurity.
• Strong expertise in:
• AWS IAM
• AWS KMS
• AWS Security Hub
• Amazon GuardDuty
• AWS Config
• AWS WAF
• AWS Network Firewall
• Amazon VPC Security

Infrastructure as Code (IaC)

• CloudFormation
• Terraform
• AWS CDK

Cloud Security Fundamentals

• Zero Trust Architecture
• Least Privilege Access
• Encryption
• Data Protection
• Network Security

Automation & DevSecOps

• Python or Bash scripting
• Secure CI/CD pipelines
• Artifact scanning
• Secrets Management
• Pipeline hardening
• SIEM integration and centralized logging

Networking & Compliance

• Firewalls
• Proxies
• Network Segmentation
• DLP
• Isolation Solutions
• Compliance frameworks:
• GDPR
• HIPAA
• PCI DSS
• SOC 2

Data Protection

• Data Classification
• DLP Controls
• Encryption Strategy
• Secure Data Lifecycle Management

Soft Skills

• Strong analytical and troubleshooting skills
• Excellent communication and stakeholder management
• Cross-functional collaboration

Preferred Certification

• AWS Certified Security – Specialty

Good to Have Skills

• Multi-cloud security (AWS + Azure / GCP)
• ECS / EKS Container Security
• AWS Macie
• AWS Detective
• Advanced Data Governance
• Zero Trust implementations
• Azure AD / Okta SSO
• SOAR / Playbook Automation
• Splunk, Sumo Logic, Datadog
• Additional security certifications (CISSP, CISM, CCSP)
• SOC Operations (Tier 1/2/3)
• Security Incident Management
• Threat Intelligence
• Playbook Automation
• Incident Investigation, Containment, Recovery & Post-Incident Reviews

As a Senior Data Engineer, you will be responsible for designing, developing, and optimizing scalable cloud-native data platforms on AWS. You will build high-performance batch and analytical data pipelines using DBT (Cloud + Core), AWS Glue, SMUS, Redshift, Python, MWAA, APIs, and other AWS services while ensuring reliability, scalability, and operational excellence.

The role requires strong expertise in data engineering, SQL optimization, distributed data processing, and cloud-native architectures.

Must Have Primary Skills

Data Engineering

• 3+ years of hands-on experience building large-scale AWS Data Engineering solutions.
• Strong experience designing scalable batch and streaming data pipelines on AWS.
• Experience implementing monitoring, logging, alerting, and observability for production data pipelines.
• Strong understanding of data quality, troubleshooting, debugging, and production support.

DBT & Data Warehousing

• Strong hands-on experience with DBT (Cloud + Core) (Highly Recommended).
• Hands-on expertise with Amazon Redshift for Data Warehousing and Analytics.

Programming & Querying

• Strong expertise in:
• SQL (Analytical Queries, Window Functions, Stored Procedures)
• Python
• Spark / PySpark
• Strong experience in Spark/PySpark performance tuning and optimization.

Lakehouse & Apache Iceberg

• Hands-on experience designing and implementing Lakehouse solutions using Apache Iceberg.
• Experience optimizing Apache Iceberg and Amazon Aurora PostgreSQL for performance and scalability.
• Strong understanding of:
• Data Modeling
• Partitioning
• File Formats
• Lakehouse / Data Lake Architectures

AWS Cloud Services

Hands-on experience with AWS services including:

• AWS Glue
• Amazon MWAA
• Amazon EMR
• Amazon Redshift
• Amazon S3
• Amazon Athena
• AWS Glue Catalog
• Amazon Aurora PostgreSQL
• AWS Lambda
• Amazon EC2
• Amazon DynamoDB
• Amazon API Gateway
• Amazon CloudWatch
• Amazon SQS
• Amazon SNS
• Amazon EventBridge
• AWS Secrets Manager
• AWS IAM

Development Practices

• Experience with CI/CD.
• Strong knowledge of Git.
• Familiarity with Data Engineering best practices.

Soft Skills

• Strong communication skills.
• Excellent problem-solving ability.
• Stakeholder management experience.

Preferred Certification

• AWS Data Engineer Associate or equivalent AWS Certification.

Good to Have Skills

• AWS Step Functions
• Terraform / Infrastructure as Code (IaC)
• Kafka
• Hive
• HDFS
• Other Big Data technologies
• Experience using GenAI-assisted development tools such as:
• GitHub Copilot
• Cursor
• Kiro
• Similar AI coding assistants
• ClickHouse Database
• Telecom / Mobile Network domain knowledge
• AtScale design and development
• Docker
• Kubernetes 

We are looking for an experienced Application Developer to design, develop, enhance, and maintain cloud-native applications on AWS. The ideal candidate should have strong experience in modern frontend development using Next.js/Express.js and TypeScript, backend development using Python, and serverless application development on AWS.

The role involves:

• Developing and maintaining frontend applications using Next.js, React, and TypeScript.
• Developing backend services using Python and serverless AWS technologies.
• Designing and implementing AWS Lambda functions.
• Developing workflows using AWS Step Functions.
• Designing and maintaining Amazon AppSync GraphQL schemas and resolvers.
• Implementing multi-tenancy across application services.
• Building reusable, scalable, and responsive frontend components.
• Integrating frontend applications with backend APIs and cloud services.
• Participating in architecture discussions and technical design reviews.
• Debugging production issues and optimizing performance.
• Collaborating with architects, DevOps, data engineering, QA, and business teams.
• Following coding standards, cloud-native best practices, and contributing to documentation and code reviews.

Must Have Skills:

Frontend Development

• Strong JavaScript / TypeScript experience
• Hands-on experience with Next.js / Express.js
• Strong React.js proficiency
• Experience with Redux (or equivalent state management libraries)
• Good knowledge of HTML5, CSS3, and Material UI
• Experience building reusable and responsive frontend components

Backend Development

• Strong Python development experience
• Experience with Python frameworks:
• FastAPI
• Flask
• Experience building RESTful APIs and backend services

AWS & Cloud Development:

Hands-on experience with serverless AWS services including:

• AWS Lambda
• AWS DynamoDB
• AWS Step Functions
• Amazon API Gateway
• Amazon AppSync
• Amazon SQS
• Amazon SNS
• Amazon S3
• Amazon Aurora PostgreSQL
• AWS Glue (Python Shell)
• Amazon Cognito
• Amazon CloudFront

Additionally:

• Experience deploying and maintaining cloud-native applications on AWS

Collaboration

Ability to work effectively with:

• Solution Architects
• DevOps Engineers
• Data Engineers
• QA Teams
• Product Owners
• Clients and Business Stakeholders

Other Qualifications

• Experience with AWS CDK
• Exposure to microservices architecture
• Knowledge of caching, queuing, and application performance optimization
• Exposure to AWS Well-Architected Framework and application security best practices
• Experience working on data-intensive applications
• Experience in effort estimation, documentation, and sprint planning
• Experience building highly scalable and high-performance cloud applications.

Key Responsibilities

• Implement enhancements and features for business users to support audit data corrections through the web application.
• Develop new features across web applications and internal systems, including frontend enhancements, forms, and dynamic content.
• Manage databases to ensure data integrity and validation.
• Maintain database schemas and data linkages across systems.
• Perform unit testing of implemented solutions.
• Execute UI enhancements and navigation updates.
• Maintain existing code, contribute to documentation, and collaborate with cross-functional teams. 

Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed. We are passionate about our customers and obsessed with problem-solving to make products smarter, customer experiences frictionless, processes autonomous and businesses safer by detecting risks, threats and anomalies. Together with partners and customers, we embark on a data and AI led transformation journey that delivers impactful and measurable results.

Job Overview:

We are seeking a highly skilled DBA, AWS Database Migration & Data Engineering Consultant with strong Oracle/SQL Server expertise to lead the rehosting and modernization of enterprise databases into AWS. This role will primarily focus on Oracle/SQL Server database optimization, transaction retention, and seamless migration continuity, especially in environments leveraging services/tools like Oracle Exadata, Oracle Data Integrator (ODI), and similar tools.

The ideal candidate combines deep experience in Oracle/SQL Server database platforms, large-scale migration strategies, and modern AWS data engineering practices, ensuring minimal downtime, data integrity, and performance optimization during migration.

Key Responsibilities:

• Lead end-to-end SQL Server database migration from on-prem (including Exadata) to AWS platforms such as Amazon RDS, EC2, and Aurora PostgreSQL-compatible environments.
• Ensure transaction continuity, CDC-based replication, and minimal downtime cutovers.
• Perform Oracle database performance tuning, indexing strategies, and query optimization prior to migration.
• Design and execute schema conversion and validation strategies (Oracle to PostgreSQL/MySQL where applicable).
• Leverage AWS DMS and SCT for full load and continuous replication migrations.
• Work with ODI pipelines and existing ETL workflows, ensuring compatibility or modernization on AWS (Glue/EMR).
• Design and implement secure, highly available database architectures on AWS.
• Build and optimize data pipelines using AWS Glue, PySpark, or EMR.
• Manage S3-based data lakes and metadata using Glue Data Catalog.
• Collaborate with application, infrastructure, and analytics teams for smooth migration and post-migration stabilization.
• Implement data validation, reconciliation, and rollback mechanisms.
• Ensure compliance with security, governance, and regulatory standards (GDPR, HIPAA, etc.).

Must Have:

• 7+ years of experience in database administration and migration (SQL Server, Oracle, MySQL).
• Experience migrating Oracle, SQL Server, and MySQL databases to AWS (RDS, EC2, Aurora).
• Hands-on with AWS DMS and SCT for full load and CDC replication.
• Strong experience in database administration (HA, DR, backup/restore, schema conversion).
• Experience building pipelines using AWS Glue, PySpark, or Spark on EMR.
• Strong experience with S3-based data lakes and Glue Data Catalog.
• Strong SQL, Python/PySpark, and shell scripting.

Nice to Have:

• Experience with Aurora Global Database and cross-region replication.
• Familiarity with Oracle GoldenGate for real-time replication.
• Knowledge of Data Mesh architecture and cross-account data sharing in AWS.
• Experience with Redshift, Lake Formation, and modern data warehousing.
• Exposure to CI/CD pipelines for database and ETL deployments (CodePipeline, Jenkins).
• AWS Certifications (Solutions Architect Associate / Data Engineer Associate).
• Experience mentoring teams and driving knowledge transfer sessions.
• Experience with Infrastructure as Code (Terraform / CloudFormation).

As an Associate Technical Architect - Data, you will lead the end-to-end design, architecture, and implementation of enterprise-scale AWS data platforms and Lakehouse solutions. You will provide technical leadership across the entire project lifecycle—from solution architecture and technology selection to implementation, optimization, deployment, and production support.

The role requires deep hands-on expertise in modern AWS data engineering technologies, strong architectural skills, and the ability to mentor engineering teams while collaborating with business and technical stakeholders to deliver scalable, secure, and high-performance data solutions.

Must Have Skills:

• 8+ years of experience designing and delivering enterprise-scale Data Lake, Lakehouse, or Data Warehouse solutions on AWS.
• Proven experience leading end-to-end implementation of cloud-native data platforms, including architecture, design, development, deployment, and production support.
• Strong hands-on expertise in SQL (analytical queries, window functions, stored procedures), Spark/PySpark, and Python.
• Strong hands-on experience designing and implementing Lakehouse architectures using Apache Iceberg.
• Strong knowledge of AWS services including EMR, S3, Athena, Glue Catalog, Aurora PostgreSQL, Lambda, CloudWatch, SQS, SNS, EventBridge, IAM, and related AWS data services.
• Experience designing and implementing scalable batch and streaming data pipelines using AWS native services.
• Strong expertise in Spark/PySpark performance tuning and optimization.
• Hands-on experience optimizing Apache Iceberg and Aurora PostgreSQL for performance, scalability, and cost efficiency.
• Strong understanding of data modeling, distributed data processing, partitioning strategies, file formats, and Lakehouse/Data Lake architectures.
• Strong understanding of AWS architecture principles, including security, networking, disaster recovery, scalability, resiliency, and cost optimization.
• Experience designing orchestration workflows using Apache Airflow or AWS Step Functions.
• Ability to define cloud data platform architectures, evaluate technology choices, and articulate architectural trade-offs and best practices.
• Experience leading globally distributed engineering teams, mentoring developers, conducting architecture/code reviews, and driving engineering best practices.
• Excellent communication, stakeholder management, problem-solving, and technical leadership skills with the ability to translate business requirements into scalable technical solutions.
• AWS Solution Architect Associate/Professional or AWS Data Engineer Associate certification is preferred.

Good to Have Skills:

• Experience with ClickHouse, including performance tuning and query optimization.
• Experience with Infrastructure as Code using Terraform or CloudFormation.
• Experience implementing CI/CD pipelines for data engineering workloads.
• Exposure to Kafka, Hive, HDFS, or other Big Data technologies.
• Hands-on experience using GenAI-assisted development tools such as Kiro, GitHub Copilot, Cursor, or similar AI coding assistants to improve engineering productivity.
• Experience integrating with data governance and metadata management tools such as Collibra.
• Experience integrating with data virtualization platforms such as Denodo.
• Telecom/Mobile Network domain knowledge is preferred but not mandatory.

We are seeking an innovative and experienced Machine Learning Engineer at Architect level with a strong foundation in both traditional data science and modern Generative AI. The ideal candidate will lead the design, development, and deployment of high-impact, data-driven solutions on our Azure cloud infrastructure. You will be responsible for architecting complex systems, including multi-agent platforms and computer vision solutions, optimizing legacy models, and providing technical leadership to cross-functional teams to solve challenging business problems.

Must-Have Skills & Experience:

• Proven experience architecting, developing, and deploying traditional and deep learning solutions at scale, from concept to production.
• Lead end-to-end ML lifecycle including data preparation, feature engineering, model development, validation, deployment, and monitoring.
• Provide technical leadership, mentorship, and architecture-level guidance to project teams.
• Demonstrated expertise in designing and implementing complex multi-agent systems.
• Experience with agentic design patterns such as supervisor-worker and orchestrator-led group chats to automate intricate business processes (e.g., invoice processing, document automation).
• Experience with data augmentation techniques and human-in-the-loop annotation processes for large-scale model training.
• Evaluate and optimize existing models using traditional ML techniques. Proven expertise in traditional ML algorithms (regression, decision trees, SVM, ensemble models, clustering, Random Forest, XGBoost).
• Deep understanding of ML pipeline orchestration and model lifecycle management with production-grade implementation experience.
• Ensure adherence to MLOps best practices and drive implementation on Azure cloud.
• Extensive experience in Azure cloud services including Azure Machine Learning, Azure Data Factory, Blob Storage, Azure DevOps, and Azure Container Apps.
• Leveraged Azure Cognitive Search and Azure OpenAI Service to build scalable and efficient knowledge retrieval systems, enabling real-time semantic search and contextual answer generation.
• Designed and implemented RAG pipelines on Microsoft Azure, integrating large language models (LLMs) with domain-specific knowledge bases to enhance AI-driven information retrieval and response accuracy.
• Experience with evaluation, monitoring and observability frameworks for Agentic workflows.
• Experience designing fault-tolerant systems with robust error handling, fallback mechanisms, and state management for complex, multi-step AI workflows.
• Ability to design and review ML architecture and system integration strategies with hands-on experience in production deployments.
• Certifications in Azure AI Engineer or Azure Solutions Architect.
• Excellent problem-solving, communication, and stakeholder management skills with experience presenting technical solutions to business stakeholders.

Good-to-Have Skills:

• Collaboration skills with data scientists, data engineers, and product stakeholders to convert business requirements into scalable ML models.
• Contributions to open-source projects.

We are seeking an experienced MLOps Architect who can drive end-to-end implementation of the proposal being prepared for the initiative and also contribute broadly across other enterprise AI/ML programs. This role demands a strong architectural mindset, hands-on technical depth, and the ability to design scalable, cloud-native machine learning operations across traditional ML and modern LLM workflows.

The ideal candidate will bring experience with SageMaker-based MLOps pipelines, evaluation of equivalent tooling stacks, hybrid MLOps/LLMOps automation, CI/CD orchestration, governance, and production-grade scalability patterns.

Must have skills & Qualifications:

• 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
• Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), ECS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
• Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon.
• Deep understanding of model lifecycle management (feature engineering->training -> registry -> deployment -> monitoring).
• Experience implementing or supporting LLMOps pipelines, including: prompt versioning, evaluation metrics, automation frameworks.
• Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
• Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
• Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
• Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
• Experience with feature engineering pipelines and Feature Store management.
• Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
• Hands-on experience with Bedrock, OpenAI, Anthropic, or Llama models.
• Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
• Strong foundation in Python and cloud-native development patterns.
• Solid understanding of security best practices, IAM, secrets management, and artifact governance.

Good to have skills:

• Experience with vector databases, RAG pipelines, or multi-agent AI systems.
• Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK).
• Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments.
• Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry).
• Knowledge of Lakehouse (Delta/Iceberg/Hudi) architecture.
• Ability to translate business goals into scalable AI/ML platform designs.
• Strong communication and cross-team collaboration skills.
• Ability to guide engineering teams through technical uncertainty and design choices.

Key Responsibilities:

• Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation.
• Build container-oriented ML platforms (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.).
• Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems.
• Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks.
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
• Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions.
• Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling cloud services, Kubernetes workloads, and MLOps tools.
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms.
• Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices. 

Associate Technical Architect / Senior Software Developer – Java, J2EE, Spring Boot, AWS

At Quantiphi, you will leverage your expertise in Java, Spring Boot, and AWS to develop, modernize, and enhance enterprise applications. The role involves supporting Java application modernization initiatives, designing scalable cloud-native solutions, and delivering high-quality software for enterprise customers.

• Experience in Java application modernization, including upgrading legacy applications to newer Java LTS versions (Java 17/21/25), is preferred.
• Must have experience in developing enterprise-level software and web applications using J2EE/Spring Boot.
• Experience in API integration, application deployment, cloud architecture, and application security.
• Well-experienced in working with tools like Git, Maven, JFrog.
• Should have a solid understanding of Object-Oriented Programming (OOP).
• Well-experienced to perform Unit Testing and Integration Testing and have good experience in Agile-based development approach.
• Expertise in developing enterprise-level web applications and REST and SOAP APIs using Microservices, with demonstrable production-scale experience.
• Working knowledge of SQL or NoSQL databases.
• Understand Architecture Requirements and ensure effective design, development, validation, and support activities with strong problem-solving ability.
• Understanding of core AWS services, uses, and basic AWS architecture best practices.
• Exposure to AWS Transform Custom (ATX) or similar automated code modernization tools is an added advantage.
• Proficiency in developing, deploying, and debugging cloud-based applications using AWS.
• Ability to use the AWS service APIs, AWS CLI, and SDKs to write applications.
• Ability to identify key features of AWS services.
• Experience in developing enterprise web applications using Angular, TypeScript, and REST API integration.
• Identify bottlenecks and bugs, and recommend solutions by comparing the advantages and disadvantages of custom development.
• Should contribute to team meetings, troubleshooting development and production problems across multiple environments and operating platforms.
• Execute strong collaboration and communication skills within distributed project teams, with the ability to prioritize and multitask.

ROLE & RESPONSIBILITIES

• Associate Technical Architect will be responsible for connecting with clients for project briefing, consultation, installation, and close-out reviews.
• Support the design, development, enhancement, and modernization of enterprise Java applications, collaborating with clients and internal teams to deliver high-quality software solutions.
• Help out in managing customer demands to ensure maximum satisfaction and to maintain quality.
• Engage in the negotiation of customer job demands and specifications as regards to resources and infrastructure, and assist in creating comprehensive technical documents.
• Supervise activities between internal and external resources, and facilitate smooth workflow for service delivery.
• Evaluate project data for accuracy, and take the lead in setting project targets and priorities. Give training and mentorship to team members to make them better on the job.
• Be up to date with current field practices to enhance service standards and delivery.
• Review customer technical demands and instructions and assist the internal team and customers to identify the best order for lifts and the most efficient methods of maintaining and using delivered materials.
• Remain knowledgeable about current technology and carry out research to identify new trends that can be used to achieve maximum results.
• Carry out other technical-related duties that may be required. Be abreast of the best coding and architecting practices, ensuring the APIs are robust and easy to maintain, and coordinate with the rest of the team working on different layers of the infrastructure.

At Quantiphi, you will be a key leader within our Platform Engineering team, responsible for defining and driving the architectural vision for cloud-based solutions. You will lead the design and implementation of robust pipeline infrastructure, orchestrating integration flows among upstream RPA processes, the AR tool backend, and downstream systems. You will work closely with stakeholders, cross-functional teams, and clients to ensure that solutions are scalable, secure, and aligned with business objectives.

Must have skills:

• Strong expertise in designing and architecting scalable, secure, and highly available AWS platforms using services such as API Gateway, Lambda, EC2, ECS/EKS, VPC, IAM, S3, CloudWatch, CloudTrail, ECR, SNS, SQS, EventBridge, Systems Manager (SSM), KMS, and Secrets Manager.
• Proven technical experience in designing and overseeing end-to-end platform integration architectures, orchestrating data and process flows between upstream systems, third-party applications (e.g., ERP systems such as BaaN, SAP, Oracle), internal services, and downstream consumer applications.
• Strong expertise in API architecture, including RESTful APIs, API Gateway, authentication/authorization (OAuth, JWT, API Keys), API lifecycle management, integration patterns, and secure third-party connectivity.
• Extensive experience designing AWS IAM security governance, including IAM Roles, Policies, Cross-Account Access, Permission Boundaries, Service Control Policies (SCPs), and enterprise identity and access management strategies.
• Strong experience designing AWS infrastructure using Infrastructure as Code (Terraform, AWS CloudFormation, or AWS CDK), with reusable platform modules, reference architectures, and standardized deployment frameworks.
• Expertise in designing and implementing enterprise CI/CD and DevSecOps pipelines using GitHub Actions, Jenkins, GitLab CI/CD, or AWS CodePipeline, integrating automated testing, security scanning, and deployment governance.
• Strong knowledge of enterprise networking, including VPC architecture, Transit Gateway, Direct Connect, VPN, Load Balancers, Route 53, DNS, PrivateLink, and hybrid connectivity.
• Experience implementing enterprise monitoring and observability using Amazon CloudWatch, CloudTrail, dashboards, centralized logging, and alerting frameworks to ensure platform reliability and operational excellence.
• Proficiency in automation using Python, Bash, PowerShell, and AWS SDK (Boto3) to streamline platform operations and deployments.
• Excellent technical solution design, stakeholder management, technical leadership, documentation, and mentoring skills, with the ability to drive architecture reviews and establish engineering best practices.

Good to have skills:

• Experience designing enterprise integration platforms using event-driven and microservices architectures.
• Knowledge of AWS Organizations, Control Tower, Landing Zone, governance frameworks, and FinOps best practices.
• Experience with container platforms such as Docker, Kubernetes, Amazon ECS/EKS, and service mesh technologies.
• Familiarity with enterprise observability platforms such as Grafana, Prometheus, Datadog, Splunk, or OpenSearch.
• Experience with enterprise messaging and integration technologies such as Kafka, Amazon MSK, MQ, or EventBridge.
• Knowledge of disaster recovery, business continuity, and high-availability architecture.
• AWS Certifications such as Solutions Architect – Professional, DevOps Engineer – Professional, Security Specialty, or Advanced Networking – Specialty.
• Experience working in Agile, DevOps, and Platform Engineering environments.