aws software development engineer

Intellias

<div>
 
 
 Python / AI Engineer (AWS Bedrock, Multi-Agent AI)
 
 
 
 
 About the Role
 
 
 
 
 
 
 We are looking for a
 
 Python / AI Engineer
 
 to design, build, and test intelligent multi-agent applications running on
 
 AWS Bedrock
 
 . In this role, you will develop AI agents using modern agent frameworks, implement agent collaboration and delegation patterns, integrate foundation models, and establish testing and evaluation practices for production-grade AI solutions.
 
 
 You will work closely with AI engineers, platform teams, and architects to build scalable, maintainable, and enterprise-ready agent-based applications.
 
 
 
 
 
 
 Project Overview
 
 
 
 Our customer is a multinational corporation with more than a century of history and operations in over 180 countries. One of its key strategic initiatives is the development and adoption of a new generation of Reduced-Risk Products (RRPs), targeting more than one billion consumers worldwide.
 
 
 Intellia partners with the customer to engineer a comprehensive software ecosystem supporting innovative IoT products, digital commerce, and enterprise platforms. Our engineering teams develop core platform components powering best-in-class eCommerce, Digital Marketing, and IoT solutions.
 
 
 As a
 
 Python / AI Engineer
 
 , you will become part of the
 
 Core Architecture Team
 
 , contributing to the development of a modern Digital Engineering Enterprise Platform.
 
 
 The platform provides engineering teams with reusable services, development tools, cloud technologies, governance standards, and operational best practices that accelerate software delivery across more than 700 enterprise applications.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Design, develop, and maintain AI agent applications using Python and modern agent frameworks such as LangGraph, Strands, CrewAI, or equivalent.
 </li>
 <li>
 Build intelligent multi-agent workflows with collaboration and delegation capabilities.
 </li>
 <li>
 Integrate AI agents with AWS Bedrock foundation models and related AWS AI services.
 </li>
 <li>
 Develop Agent-to-Agent (A2A) communication using JSON-RPC 2.0, Server-Sent Events (SSE), and related communication patterns.
 </li>
 <li>
 Design and implement automated testing strategies for agent behavior, workflow correctness, and system reliability.
 </li>
 <li>
 Build evaluation frameworks using pytest, LangSmith, and other testing and validation tools.
 </li>
 <li>
 Create reusable components, templates, and pattern libraries that accelerate AI application development.
 </li>
 <li>
 Produce clear technical documentation, implementation guides, and engineering best practices.
 </li>
 <li>
 Collaborate with platform, AI, DevOps, and product teams to deliver scalable and production-ready AI solutions.
 </li>
 <li>
 Monitor, troubleshoot, and optimize agent performance, reliability, and operational efficiency.
 </li>
 <li>
 Promote engineering excellence through observability, testing, governance, and AI application lifecycle best practices.
 </li>
 </ul>
 
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 Bachelor's degree in computer science, Software Engineering, or a related field.
 </li>
 <li>
 4+ years of professional Python software development experience.
 </li>
 <li>
 Hands-on experience with at least one modern agent framework such as LangGraph, Strands, Crew AI, or an equivalent framework.
 </li>
 <li>
 Experience implementing multi-agent collaboration and delegation patterns.
 </li>
 <li>
 Experience integrating applications with AWS Bedrock APIs and foundation models.
 </li>
 <li>
 Knowledge of Agent-to-Agent (A2A) communication patterns, including JSON-RPC 2.0 and Server-Sent Events (SSE).
 </li>
 <li>
 Experience building automated testing frameworks using pytest and AI evaluation tools such as LangSmith.
 </li>
 <li>
 Experience writing technical documentation and creating reusable engineering assets.
 </li>
 <li>
 Strong understanding of software engineering best practices, code quality, and testing methodologies.
 </li>
 <li>
 Excellent problem-solving, communication, and collaboration skills.
 </li>
 </ul>
 
 
 
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Hands-on experience with the Strands SDK.
 </li>
 <li>
 Experience with LangSmith tracing, evaluation, and observability.
 </li>
 <li>
 Familiarity with prompt engineering and AI evaluation methodologies.
 </li>
 <li>
 Experience deploying AI applications in AWS cloud environments.
 </li>
 <li>
 Knowledge of CI/CD pipelines, Infrastructure as Code (IaC), and cloud-native application development.
 </li>
 </ul>
 
 
 
 
 
 Why Join Us
 
 
 <ul>
 <li>
 Work on enterprise-scale AI initiatives for a global industry leader.
 </li>
 <li>
 Build intelligent multi-agent applications using the latest AI frameworks and AWS Bedrock technologies.
 </li>
 <li>
 Collaborate with experienced architects and AI engineers on innovative cloud-native solutions.
 </li>
 <li>
 Contribute to engineering standards, reusable frameworks, and best practices for enterprise AI development.
 </li>
 <li>
 Be part of a team shaping the future of production-grade AI applications at global scale.
 </li>
 </ul>
 
 
 
</div>

Senior Python / AI Engineer (AWS Bedrock, Multi-Agent AI)

Impressico Business Solutions

<div>Location: Noida / Hyderabad
Experience: 3 to 5 Years
No. Of Openings: 2</div>
 <div><div><div><h3 class="jobSectionHeader">Job Description
</h3>We are seeking a detail-oriented and analytical Product &amp; Manufacturer Data Validation Specialist to support our data quality initiatives for industrial parts. This hybrid role combines responsibilities for validating manufacturer information and ensuring accurate product taxonomy classification. The ideal candidate will work with AI-generated data, Excel workbooks, and online resources to verify and standardize manufacturer names and product categories, enhancing the integrity of our product and supplier databases.
Manufacturer Data Validation
<ul><li>Review manufacturer names extracted via AI-assisted web search and assess accuracy.</li>
<li>Conduct manual research to confirm manufacturer identities and correct discrepancies.</li>
<li>Update Excel workbooks with verified and standardized manufacturer data.</li>
<li>Flag ambiguous or unverifiable entries for further investigation.</li>
<li>Provide feedback to improve AI data extraction and enrichment workflows.</li>
</ul>Product Taxonomy Validation
<ul><li>Validate and refine product taxonomy for industrial parts, ensuring alignment with internal classification standards.</li>
<li>Review AI-generated or vendor-supplied categorizations and correct misclassifications.</li>
<li>Research product attributes to determine appropriate category placement.</li>
<li>Document taxonomy rules, exceptions, and updates for reference and training.</li>
<li>Support taxonomy mapping for new product onboarding and catalog expansion.</li>
</ul><h3 class="jobSectionHeader">Job Specification</h3>
<ul><li>Strong proficiency in Microsoft Excel, including formulas, filters, and data management tools.</li>
<li>Excellent research and analytical skills with a high attention to detail.</li>
<li>Experience working with AI-generated data or web scraping tools is a plus.</li>
<li>Familiarity with manufacturer databases, industrial parts, or technical product data.</li>
<li>Understanding of product taxonomy, classification systems, and metadata.</li>
<li>Ability to work independently and manage multiple tasks effectively.</li>
<li>Strong communication skills for cross-functional collaboration.</li></ul></div></div></div><div></div>

Product & Manufacturer Data Validation Specialist

<div>
 
 
 Senior Python Engineer &ndash; Multi-Agent AI (AWS, LangGraph)
 
 
 
 
 
 
 
 About the Role
 
 
 
 We are looking for a
 
 Senior Python Engineer
 
 to design and build enterprise-grade
 
 multi-agent AI systems
 
 on AWS. In this role, you will architect and develop intelligent agent solutions using
 
 LangGraph
 
 ,
 
 LangChain
 
 , and
 
 AWS Bedrock AgentCore Runtime
 
 , focusing on scalable orchestration, stateful workflow execution, human-in-the-loop (HITL) processes, and distributed agent collaboration.
 
 
 You will work closely with architects, platform engineers, and product teams to deliver production-ready AI solutions that are reliable, scalable, and aligned with enterprise engineering standards.
 
 
 
 
 
 
 Project Overview
 
 
 
 Our customer is a multinational corporation with more than a century of history and operations in over 180 countries. One of its key strategic initiatives is the development and adoption of a new generation of Reduced-Risk Products (RRPs), targeting more than one billion consumers worldwide.
 
 
 Intellia partners with the customer to engineer a comprehensive software ecosystem supporting innovative IoT products, digital commerce, and enterprise platforms. The engineering organization develops core platform components powering best-in-class eCommerce, Digital Marketing, and IoT solutions.
 
 
 As a
 
 Senior Python Engineer
 
 , you will join the
 
 Core Architecture Team
 
 , contributing to the design and implementation of a modern Digital Engineering Enterprise Platform.
 
 
 The platform consists of services and applications that accelerate software delivery by providing engineering teams with reusable technologies, standardized development practices, compliance controls, and operational capabilities across more than 700 enterprise applications.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Design, develop, and maintain enterprise-grade multi-agent AI applications using Python, LangGraph, and LangChain.
 </li>
 <li>
 Build scalable agent workflows leveraging AWS Bedrock AgentCore Runtime.
 </li>
 <li>
 Implement advanced orchestration patterns, including supervisor/worker, fan-out/fan-in, and collaborative multi-agent architectures.
 </li>
 <li>
 Design stateful workflow execution using LangGraph checkpointing, recovery, and persistence mechanisms.
 </li>
 <li>
 Develop resilient, fault-tolerant, and idempotent execution strategies for long-running AI workflows.
 </li>
 <li>
 Integrate Human-in-the-Loop (HITL) approval processes into business-critical agent workflows.
 </li>
 <li>
 Enable agent delegation and collaboration through Agent Gateway and Agent-to-Agent (A2A) communication.
 </li>
 <li>
 Implement memory, context management, and persistence strategies for AI agents.
 </li>
 <li>
 Optimize workflow scalability, reliability, observability, and performance within distributed cloud environments.
 </li>
 <li>
 Collaborate with platform, security, DevOps, and product teams to deliver production-ready AI solutions.
 </li>
 <li>
 Define engineering standards, testing strategies, and operational best practices for agent-based applications.
 </li>
 <li>
 Support deployment, monitoring, troubleshooting, and continuous improvement of AI solutions running on AWS.
 </li>
 </ul>
 
 
 Requirements
 
 
 <ul>
 <li>
 Bachelor's degree in computer science, Software Engineering, or a related field.
 </li>
 <li>
 5+ years of professional Python software engineering experience.
 </li>
 <li>
 Hands-on experience building multi-step agent workflows with LangGraph.
 </li>
 <li>
 Strong experience with LangChain.
 </li>
 <li>
 Experience with AWS Bedrock AgentCore Runtime.
 </li>
 <li>
 Solid understanding of multi-agent orchestration patterns, including supervisor/worker and fan-out/fan-in architectures.
 </li>
 <li>
 Experience implementing LangGraph checkpointing and state management.
 </li>
 <li>
 Knowledge of Human-in-the-Loop (HITL) workflow design.
 </li>
 <li>
 Experience with Agent-to-Agent (A2A) delegation through Agent Gateway.
 </li>
 <li>
 Experience with workflow orchestration platforms such as Temporal, Apache Airflow, or similar.
 </li>
 <li>
 Strong understanding of distributed systems concepts, including checkpointing, idempotency, fault tolerance, and workflow recovery.
 </li>
 <li>
 Experience building scalable, cloud-native applications on AWS.
 </li>
 <li>
 Strong problem-solving, communication, and collaboration skills.
 </li>
 </ul>
 
 
 
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Experience evaluating AWS AgentCore Runtime maturity and enterprise adoption.
 </li>
 <li>
 Hands-on experience with AWS Bedrock Memory API.
 </li>
 <li>
 Experience integrating enterprise approval workflows into Human-in-the-Loop AI systems.
 </li>
 <li>
 Experience with AI observability, monitoring, and production operations.
 </li>
 <li>
 Familiarity with CI/CD pipelines and Infrastructure as Code in AWS environments.
 </li>
 </ul>
 
 
 
 
 
 Why Join Us
 
 
 <ul>
 <li>
 Work on enterprise-scale AI initiatives for a global industry leader.
 </li>
 <li>
 Design and build next-generation multi-agent AI systems using cutting-edge AWS technologies.
 </li>
 <li>
 Influence architecture decisions and engineering best practices across large-scale platforms.
 </li>
 <li>
 Collaborate with highly experienced architecture and engineering teams on innovative cloud-native solutions.
 </li>
 <li>
 Opportunity to shape the future of enterprise AI orchestration in production environments.
 </li>
 </ul>
 
 
 
</div>

Senior Python Engineer – Multi-Agent AI (AWS, LangGraph)

Occam Solutions

<div>Primary Responsibility 
The DevSecOps Engineer shall serve as the lead technical engineer responsible for designing, implementing, securing, automating, and sustaining the cloud infrastructure supporting the Office of Naval Research (ONR) Data &amp; Analytics environment. The DevSecOps Engineer shall lead the implementation and execution of the technical requirements identified in PWS Section 4.5 &ndash; Technical Infrastructure and Platform Support, including infrastructure automation, Continuous Integration/Continuous Deployment (CI/CD), cloud security, container orchestration, Infrastructure-as-Code (IaC), and operational sustainment of the FedRAMP High and DoD Impact Level 5 (IL5) cloud environment. 
The DevSecOps Engineer shall architect and maintain secure cloud infrastructure, automate deployment pipelines, support AI/ML platform integration, implement cloud-native security controls, monitor system health and security events, and ensure compliance with DoD cybersecurity requirements. This position will work closely with software developers, data engineers, cloud architects, cybersecurity personnel, and Government stakeholders to deliver resilient, scalable, and secure cloud services. 
Duties and Responsibilities

The DevSecOps Engineer shall:
<ul>
<li>Design, deploy, automate, and maintain secure cloud infrastructure supporting mission applications and analytics platforms.</li>
<li>Develop, implement, and manage automated CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, or equivalent technologies.</li>
<li>Build and maintain Infrastructure-as-Code (IaC) solutions utilizing Terraform, Ansible, or similar automation frameworks.</li>
<li>Implement and manage containerized application environments using Docker, Kubernetes, or comparable orchestration platforms.</li>
<li>Configure, monitor, and optimize cloud-native security services including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), endpoint protection, and Security Information and Event Management (SIEM) platforms.</li>
<li>Analyze security logs, audit events, vulnerability reports, and SIEM alerts to identify threats, recommend mitigation strategies, and support incident response activities.</li>
<li>Support continuous Authority to Operate (ATO) compliance through configuration management, security patching, vulnerability remediation, and security documentation.</li>
<li>Implement secure API management, identity and access management (IAM), secrets management, encryption, and least-privilege access controls.</li>
<li>Develop automated monitoring, logging, alerting, and operational dashboards to improve system reliability and performance.</li>
<li>Support deployment and operationalization of AI/ML platforms and services, including cloud-native machine learning environments.</li>
<li>Collaborate with software developers, data engineers, and cloud architects to integrate security throughout the software development lifecycle (SDLC).</li>
<li>Ensure cloud infrastructure complies with DoD cybersecurity policies, FedRAMP High requirements, DoD IL5 controls, and applicable security frameworks.</li>
<li>Support system scalability, resilience, disaster recovery, and operational continuity.</li>
<li>Document system architectures, deployment procedures, security configurations, and operational processes.</li></ul> 


Minimum Qualifications

The DevSecOps Engineer shall meet the requirements for the &quot;DevSecOps Engineer &ndash; Intermediate&quot; labor category as defined in the CHESS ITES-3S contract vehicle and possess the following additional qualifications:
<ul>
<li>Seven (7) years of combined experience in DevSecOps, DevOps, SecOps, or MLOps engineering and development.</li>
<li>Five (5) years of hands-on experience designing, securing, implementing, and maintaining cloud environments, preferably within AWS GovCloud, Azure Government, or similarly regulated cloud environments.</li>
<li>Demonstrated experience supporting cloud environments operating under FedRAMP High, DoD Impact Level 5 (IL5), or comparable federal security requirements.</li>
<li>Verifiable experience on at least two (2) projects designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Jenkins, GitLab CI/CD, Azure DevOps, GitHub Actions, or equivalent technologies.</li>
<li>Demonstrated expertise implementing and managing containerization and orchestration technologies, including Docker, Kubernetes, OpenShift, or similar platforms.</li>
<li>Hands-on experience implementing Infrastructure-as-Code (IaC) using Terraform, Ansible, CloudFormation, or comparable automation tools.</li>
<li>Experience configuring and administering cloud-native security capabilities including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), Security Information and Event Management (SIEM), vulnerability management, and cloud security monitoring solutions.</li>
<li>Verifiable experience analyzing security logs, SIEM data, audit records, and threat intelligence to identify vulnerabilities, investigate security events, and implement corrective actions.</li>
<li>Experience implementing secure Identity and Access Management (IAM), encryption, secrets management, and Zero Trust security principles.</li>
<li>Experience supporting automated monitoring, logging, system performance optimization, and operational sustainment of enterprise cloud platforms.</li>
<li>Strong understanding of DevSecOps best practices, secure software development lifecycle (SSDLC), and cloud security architectures.</li> 
</ul>
Qualifications
<ul>
<li>Active Secret security clearance (required).</li>
<li>Current DoD 8140 (or legacy DoD 8570) cybersecurity certification applicable to privileged access functions (e.g., Security+, CySA+, CASP+, CISSP, CCSP).</li>
<li>AWS Certified DevOps Engineer &ndash; Professional, AWS Security Specialty, Azure DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), or comparable cloud certifications.</li>
<li>Experience supporting AI/ML environments, including AWS SageMaker, Azure Machine Learning, or similar platforms.</li>
<li>Experience with streaming data technologies such as Apache Kafka, AWS Kinesis, or equivalent event-driven architectures.</li>
<li>Familiarity with NIST RMF, DISA STIGs, FedRAMP, Zero Trust Architecture, and DoD cybersecurity compliance requirements.</li></ul></div>

DevSecOps Engineer

Integrant

<div>We are seeking a skilled and experienced Principal DevOps/SysOps Engineer to join our innovative team. The ideal candidate possesses a strong knowledge in Linux Servers administration, Managing clusters, monitoring tools, containerization (Docker and Kubernetes), cloud platforms (AWS, Azure, Google), infrastructure-as-code (Terraform and Ansible), and scripting languages using Bash and Python. If you are a seasoned professional with a deep understanding of the software development lifecycle, we invite you to apply and contribute to our dynamic and collaborative environment.
If you are a passionate and experienced Principal DevOps/SysOps Engineer with a comprehensive skill set, including expertise in Linux, we encourage you to apply and contribute to our cutting-edge projects and initiatives.
Responsibilities:<ul><li>
Deploy, configure, and maintain Linux and Windows servers across physical and virtual environments.</li><li>
Strong knowledge of HPC cluster management, job scheduling systems like SLURM or Run:AI</li><li>
Hands on experience of Docker containers and Kubernetes, implementation, administration and optimization within complex large scale environments</li><li>
Proven experience as a DevOps Engineer with a focus on CI/CD, containerization, GitOps and cloud technologies.</li><li>
Proficiency in scripting languages such as Bash, Python for automation and performance tuning.</li><li>
Experience with high-speed interconnects (Infiniband, Ethernet), distributed storage systems, and cluster networking.</li><li>
Configure and maintain firewalls, load balancers, and VPNs.</li><li>
Proven ability to troubleshoot complex system and networking issues in large-scale clusters.</li><li>
Install and utilize monitoring tools to ensure the health, performance, and security of systems.</li><li>
Worked with at least one major cloud provider (AWS, Azure, or Google) to deploy and manage cloud-based solutions.</li><li>
Apply knowledge of cybersecurity best practices to enhance the security posture of systems.</li><li>
Utilize infrastructure-as-code tools like Terraform and Ansible for efficient and reproducible infrastructure deployment.</li><li>
Possess a good understanding of the software development lifecycle to collaborate effectively with development teams.</li></ul> 

Requirements 
<ul><li>
Bachelor's degree in Computer Science, Information Technology, or a related field with 5+ years of experience.</li><li>
Proven experience as a DevOps Engineer with a focus on CI/CD, containerization, and cloud technologies.</li><li>
Strong knowledge and hands-on experience with Docker containers and Kubernetes.</li><li>
Interaction with at least one major cloud provider (AWS, Azure, or Google).</li><li>
Understanding and application of cybersecurity principles.</li><li>
Proficiency in infrastructure-as-code tools like Terraform and Ansible.</li><li>
Excellent scripting skills in Bash and Python.</li><li>
Familiarity with the software development lifecycle.</li><li>
Strong knowledge of Linux operating systems.</li></ul>
Preferred Experience:<ul><li>
CKA/CKAD/CKS certification.</li><li>
Certifications in relevant DevOps, cloud, and cybersecurity domains.</li><li>
Additional scripting languages and automation tool proficiency.</li><li>
Active participation in community forums or open-source projects.</li></ul> 

Benefits 
<ul>
<li>Salary paid in USD</li>
<li>Six-month career advancing opportunities</li>
<li>Employee parking space</li>
<li>Supportive and friendly work environment</li>
<li>Premium medical insurance [employee +family]</li>
<li>English language development courses</li>
<li>Interest-free loans paid over 2.5 years</li>
<li>Technical development courses</li>
<li>Planned overtime program (POP)</li>
<li>Employment referral program</li>
<li>Premium location in Maadi</li>
<li>Social insurance</li></ul></div>

Principal SysOps Engineer

<div>
 
 
 Tech Lead Engineer &ndash; AI Registry &amp; Discovery Platform (AWS AgentCore)
 
 
 
 
 About the Role
 
 
 
 
 
 
 We are looking for a
 
 Tech Lead Engineer
 
 to design and lead the implementation of an enterprise-grade
 
 AI Registry and Discovery Platform
 
 for AI agents, tools, and MCP services. This role focuses on service registration, metadata governance, semantic discovery, identity mapping, lifecycle management, and integration with the AWS AgentCore ecosystem.
 
 
 As a technical leader, you will drive the architecture and development of a scalable platform that enables secure discovery, governance, and lifecycle management of AI capabilities across the enterprise. You will collaborate with platform, security, and AI engineering teams to establish engineering standards, governance processes, and best practices for enterprise AI platforms.
 
 
 The ideal candidate has strong platform engineering expertise, experience building large-scale registry or catalog systems, and a deep understanding of cloud-native architectures, API security, metadata management, and distributed systems.
 
 
 
 
 
 
 Project Overview
 
 
 
 Our customer is a multinational corporation with more than a century of history and operations in over 180 countries. One of its key strategic initiatives is the development and adoption of a new generation of Reduced-Risk Products (RRPs), targeting more than one billion consumers worldwide.
 
 
 Intellia partners with the customer to engineer a comprehensive software ecosystem supporting innovative IoT products, digital commerce, and enterprise platforms. Our engineering teams develop core platform components powering best-in-class eCommerce, Digital Marketing, and IoT solutions.
 
 
 As a
 
 Tech Lead Engineer
 
 , you will become part of the
 
 Core Architecture Team
 
 , contributing to the design and implementation of a modern Digital Engineering Enterprise Platform.
 
 
 The platform provides engineering teams with reusable services, cloud technologies, governance standards, and operational best practices that accelerate software delivery across more than 700 enterprise applications.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Lead the architecture, design, and implementation of an enterprise AI Registry and Discovery Platform.
 </li>
 <li>
 Design and maintain metadata schemas for MCP servers, AI tools, and agent capabilities aligned with Canonical Contract standards.
 </li>
 <li>
 Build scalable backend services using Python, FastAPI, DynamoDB, and AWS-native technologies.
 </li>
 <li>
 Develop registration, discovery, approval, certification, and lifecycle management workflows for AI agents and MCP services.
 </li>
 <li>
 Design semantic discovery capabilities using vector search, embeddings, and relevance-ranking techniques.
 </li>
 <li>
 Integrate enterprise identity systems and implement Entra Agent ID mapping across AI ecosystems.
 </li>
 <li>
 Implement secure authentication and authorization using OAuth 2.0, JWT, and enterprise identity providers.
 </li>
 <li>
 Design governance processes for onboarding, approval, certification, versioning, deprecation, and retirement of AI services.
 </li>
 <li>
 Develop APIs supporting registry operations, metadata management, discovery services, and governance workflows.
 </li>
 <li>
 Implement secure credential and secret management using HashiCorp Vault and cloud-native security controls.
 </li>
 <li>
 Design observability, auditing, compliance, and operational monitoring capabilities across the registry platform.
 </li>
 <li>
 Collaborate with AI, platform, cloud, and security teams to establish registry standards, governance policies, and engineering best practices.
 </li>
 <li>
 Mentor engineers, provide technical leadership, and support architectural decision-making across the platform.
 </li>
 <li>
 Produce architecture documentation, implementation standards, and technical governance guidelines.
 </li>
 </ul>
 
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 Bachelor's degree in computer science, Software Engineering, or a related field.
 </li>
 <li>
 5+ years of backend or platform engineering experience.
 </li>
 <li>
 Experience designing and building enterprise-scale registry, catalog, metadata management, or discovery platforms.
 </li>
 <li>
 Strong Python development experience, including Fast API and backend service development.
 </li>
 <li>
 Experience with AWS services, including AWS Agent Core Registry, Gateway, DynamoDB, and related cloud-native technologies.
 </li>
 <li>
 Experience designing metadata schemas and service registration models.
 </li>
 <li>
 Knowledge of semantic search technologies, vector databases, embeddings, and AI-powered discovery.
 </li>
 <li>
 Experience implementing secure API authentication and authorization using OAuth 2.0 and JWT.
 </li>
 <li>
 Experience integrating governance workflows, approval processes, and lifecycle management into enterprise platforms.
 </li>
 <li>
 Experience integrating enterprise identity providers and identity mapping solutions.
 </li>
 <li>
 Strong understanding of cloud-native architecture, distributed systems, API design, and platform engineering.
 </li>
 <li>
 Excellent leadership, communication, mentoring, and stakeholder management skills.
 </li>
 </ul>
 
 
 
 
 
 Nice to Have
 
 
 <ul>
 <li>
 Hands-on experience with AWS AgentCore Registry and Gateway.
 </li>
 <li>
 Experience with vector databases such as pgvector, OpenSearch, or similar semantic search technologies.
 </li>
 <li>
 Experience with HashiCorp Vault or enterprise secrets management platforms.
 </li>
 <li>
 Familiarity with MCP (Model Context Protocol) standards and Canonical Contract schema design.
 </li>
 <li>
 Experience implementing observability and distributed tracing solutions.
 </li>
 <li>
 Knowledge of CI/CD pipelines, Infrastructure as Code (IaC), and cloud security best practices.
 </li>
 </ul>
 
 Why Join Us
 
 <ul>
 <li>
 Lead the development of an enterprise AI Registry and Discovery Platform supporting next-generation AI applications.
 </li>
 <li>
 Work with cutting-edge AWS AgentCore technologies, semantic search, and cloud-native architectures.
 </li>
 <li>
 Drive engineering standards, governance, and technical strategy for enterprise AI platforms.
 </li>
 <li>
 Collaborate with experienced AI, platform, and cloud engineering teams on innovative global-scale solutions.
 </li>
 <li>
 Make a significant impact by shaping the future of AI service discovery, governance, and lifecycle management across a large enterprise.
 </li>
 </ul>
 
 
 
</div>

Python Team Lead – AI Registry & Discovery Platform (AWS AgentCore)

Datamatics Global Services Ltd

<div><h1 class="jobSectionHeader">Job Description: AI / ML Engineer</h1>
Job Title: AI / ML Engineer 
Experience: 3&ndash;11 Years 
Location: Riyadh (Onsite) 
Employment Type: Full-Time<h2 class="jobSectionHeader">
Job Overview</h2>
We are seeking a skilled AI / ML Engineer with 3&ndash;11 years of experience to design, develop, deploy, and optimize machine learning and generative AI solutions. The ideal candidate will have hands-on expertise in building scalable AI/ML models, working with cloud-native AI platforms, and implementing production-ready machine learning pipelines. Experience with modern AI frameworks, large language models (LLMs), and MLOps practices is highly desirable.<h2 class="jobSectionHeader">
Key Responsibilities</h2><ul><li>
Design, develop, train, and deploy machine learning and deep learning models for enterprise applications.</li><li>
Build and optimize end-to-end ML pipelines for data ingestion, model training, evaluation, and deployment.</li><li>
Develop Generative AI and LLM-powered applications using modern AI frameworks.</li><li>
Collaborate with data engineers, software developers, and business stakeholders to deliver AI-driven solutions.</li><li>
Deploy and monitor ML models on cloud platforms while ensuring scalability, reliability, and security.</li><li>
Optimize model performance through feature engineering, hyperparameter tuning, and continuous evaluation.</li><li>
Implement MLOps best practices including model versioning, monitoring, and CI/CD automation.</li><li>
Stay current with advancements in AI, machine learning, and cloud AI services.</li></ul><h2 class="jobSectionHeader">
Required Technical Skills</h2><h3 class="jobSectionHeader">
Cloud AI Platforms</h3><ul><li>
Hands-on experience with GCP Vertex AI or Azure Machine Learning or AWS SageMaker.</li><li>
Experience with Azure OpenAI or AWS Bedrock for Generative AI solutions.</li><li>
Experience with BigQuery ML and Dataflow for data processing and machine learning workflows.</li></ul><h3 class="jobSectionHeader">
Programming &amp; Machine Learning</h3><ul><li>
Strong proficiency in Python.</li><li>
Experience developing machine learning solutions using TensorFlow or PyTorch.</li><li>
Strong understanding of supervised, unsupervised, reinforcement learning, and deep learning concepts.</li></ul><h3 class="jobSectionHeader">
Generative AI &amp; LLM Frameworks</h3><ul><li>
Experience with Hugging Face and LangChain for building LLM-powered applications.</li><li>
Knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, and vector databases is preferred.</li></ul><h3 class="jobSectionHeader">
Data Engineering &amp; Analytics</h3><ul><li>
Experience with Databricks for data engineering, model development, and analytics workflows.</li><li>
Strong understanding of data preprocessing, feature engineering, and large-scale data processing.</li></ul><h3 class="jobSectionHeader">
MLOps &amp; Deployment</h3><ul><li>
Experience deploying machine learning models into production.</li><li>
Knowledge of Docker, Kubernetes, CI/CD pipelines, and model monitoring is an advantage.</li></ul><h2 class="jobSectionHeader">
Qualifications</h2><ul><li>
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.</li><li>
3&ndash;11 years of professional experience in AI, Machine Learning, or Data Science.</li><li>
Strong analytical, mathematical, and problem-solving skills.</li><li>
Experience working in Agile development environments.</li><li>
Excellent communication and collaboration skills.</li></ul><h2 class="jobSectionHeader">
Preferred Skills</h2><ul><li>
Experience with Large Language Models (LLMs) and Generative AI applications.</li><li>
Knowledge of Retrieval-Augmented Generation (RAG), vector databases, and AI agents.</li><li>
Experience with distributed model training and cloud-native AI architectures.</li><li>
Cloud certifications in AWS, Azure, or Google Cloud are a plus.</li></ul><h2 class="jobSectionHeader">
Key Technology Stack</h2><ul><li>
Cloud AI: GCP Vertex AI or Azure Machine Learning or AWS SageMaker</li><li>
Generative AI: Azure OpenAI or AWS Bedrock and Large Language Models (LLMs)</li><li>
Data Processing: BigQuery ML and Dataflow and Databricks</li><li>
Programming: Python</li><li>
Machine Learning Frameworks: TensorFlow or PyTorch</li><li>
LLM Frameworks: Hugging Face or LangChain</li><li>
MLOps: Docker and Kubernetes and CI/CD (Preferred)</li></ul>
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AI / ML Engineer

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