Python developer

Cloud Engineers – 2nd Shift – 4+ Years Experience

Required Qualifications:

• Strong experience in AWS infrastructure components and operational management (e.g., EC2, S3, VPC, IAM).
• Proficiency in IaC tools such as Terraform and/or CloudFormation.
• Solid scripting skills in Python, Bash, or equivalent for automation tasks.
• Experience with monitoring and logging tools (e.g., CloudWatch, Prometheus, ELK).
• Familiarity with network fundamentals: DNS, firewall rules, virtual networks.
• Strong troubleshooting mindset and experience resolving production-level issues.
• Excellent documentation and communication skills to maintain system knowledge and collaborate with teams.

Preferred Qualifications:

• AWS certifications (e.g., SysOps Administrator).
• Experience with containerization (Docker, Kubernetes/EKS/ECS).
• Exposure to CI/CD tooling like Terraform, DevOps, GitHub Actions, or any other DevOps tools.
• CI/CD pipeline, infra pipelines.
• AWS services like compute, network, storage, security.
• Migration experience like Discovery/Assessments, TA, Infra Design.

We are looking for a hands-on Senior AWS Cloud Engineer to lead the infrastructure build, optimization, automation, and production deployment of a Multi-Agent AI Chatbot Platform hosted on AWS. The development environment is already in place, and the successful candidate will drive the solution through testing, integrations, and production go-live.

Key Responsibilities

• Review, validate, and optimize existing Terraform code and AWS infrastructure.
• Establish and manage integrations with enterprise platforms such as ServiceNow, Workday, and other third-party systems.
• Design, build, and support secure, scalable, and highly available AWS environments.
• Implement and automate CI/CD pipelines and Infrastructure-as-Code practices.
• Lead infrastructure testing, performance tuning, and production readiness activities.
• Drive deployment and operationalization of the platform in the Production environment.
• Implement cloud governance, security, monitoring, and FinOps best practices.
• Troubleshoot and resolve complex cloud infrastructure issues.

Required Skills & Experience

• 10+ years of IT experience with strong expertise in AWS Cloud Engineering.
• Proven experience designing, deploying, and managing AWS production environments.
• Strong hands-on experience with Terraform and Infrastructure-as-Code.
• Experience with CI/CD pipeline automation and DevOps practices.
• Expertise in AWS services including VPC, IAM, EC2, S3, Lambda, CloudWatch, and networking.
• Experience in performance optimization, reliability, and cloud cost management (FinOps).
• Strong scripting and automation skills.
• Experience integrating enterprise applications through APIs and secure connectivity patterns.

Preferred Skills

• Experience supporting AI/GenAI, chatbot, or multi-agent platforms.
• AWS Certifications (Associate/Professional).
• Banking or Financial Services domain experience.

Job Title : AWS Data Engineer

Experience : 4+ Years

Location : Bengaluru (HSR – Hybrid, 3 Days WFO)

Notice Period : Immediate Joiner

💡 Role Overview :

We are looking for a skilled AWS Data Engineer to design, build, and scale modern data platforms. The role involves working with AWS-native services, Python, Spark, and DBT to deliver secure, scalable, and high-performance data solutions in an Agile environment.

🔥 Mandatory Skills :

Python, SQL, Spark, AWS (S3, Glue, EMR, Redshift, Athena, Lambda), DBT, ETL/ELT pipeline development, Airflow/Step Functions, Data Lake (Parquet/ORC/Iceberg), Terraform & CI/CD, Data Governance & Security

🚀 Key Responsibilities :

• Design, build, and optimize ETL/ELT pipelines using Python, DBT, and AWS services
• Develop and manage scalable data lakes on S3 using formats like Parquet, ORC, and Iceberg
• Build end-to-end data solutions using Glue, EMR, Lambda, Redshift, and Athena
• Implement data governance, security, and metadata management using Glue Data Catalog, Lake Formation, IAM, and KMS
• Orchestrate workflows using Airflow, Step Functions, or AWS-native tools
• Ensure reliability and automation via CloudWatch, CloudTrail, CodePipeline, and Terraform
• Collaborate with data analysts and data scientists to deliver actionable insights
• Work in an Agile environment to deliver high-quality data solutions

✅ Mandatory Skills :

• Strong Python (including AWS SDKs), SQL, Spark
• Hands-on experience with AWS data stack (S3, Glue, EMR, Redshift, Athena, Lambda)
• Experience with DBT and ETL/ELT pipeline development
• Workflow orchestration using Airflow / Step Functions
• Knowledge of data lake formats (Parquet, ORC, Iceberg)
• Exposure to DevOps practices (Terraform, CI/CD)
• Strong understanding of data governance and security best practices
• Minimum 4–7 years in Data Engineering (3+ years on AWS)

➕ Good to Have :

• Understanding of Data Mesh architecture
• Experience with platforms like Data.World
• Exposure to Hadoop / HDFS ecosystems

🤝 What We’re Looking For :

• Strong problem-solving and analytical skills
• Ability to work in a collaborative, cross-functional environment
• Good communication and stakeholder management skills
• Self-driven and adaptable to fast-paced environments

📝 Interview Process :

1. Online Assessment
2. Technical Interview
3. Fitment Round
4. Client Round

Associate Platform Engineer

Location: Bengaluru, India (Hybrid)

Employment Type: Full-time

Experience: 0.6-2 years (Excluding Intership)

Shift Timing: 2 PM to 11 PM IST

About Company

It is driving the electric mobility revolution through cutting-edge software, infrastructure, and professional services. Our technology empowers utilities, cities, fleets, transit agencies, and automakers to deploy EV charging infrastructure at scale safely, efficiently, and sustainably. With a global footprint spanning three continents and operations in 13 countries, we are passionate about shaping the future of sustainable transport.

Operating over 70,000 charge points globally, It is driving the transition toward cleaner, smarter, and more efficient mobility. The India team serves as a critical operational hub, supporting global platforms focused on decarbonization, digitalization, and scalable infrastructure growth.

At this company, we value purpose-driven individuals who want to make a meaningful impact and help create a cleaner, smarter, and more connected world.

Role Overview

It is seeking a Platform Engineer! We are excited to find a highly engaged engineer who is obsessed with technology that wants to be a part of a “world class” platform SRE team. Their engineers must possess an "automation first" mindset, with a relentless focus on documentation, quality, scalability, and reliability using Infrastructure as Code tools. This position will be part of a platform team that is developing exciting products and solutions and playing a key part in driving forward the electrification of transportation.

What you’ll do:  

• Ensure system reliability, uptime, and performance of global platform.
• Conduct real-time surveillance of our EV charging systems to proactively identify and mitigate performance issues and anomalies near 24/7 basis. As such, you collaborate with IDT and FMC players to ensure incident detection also happens outside office hours (monitoring shifts among team members subject to duty schedule) 
• Deliver on change & releases like firmware changes and drive insights & intelligence back into testing processes and tech discussions with the wider organization. 
•  Successfully deliver and project manage first time right commissioning activities alongside our Engineering Procurement Contract Management (EPCM) partners to successfully bring charge points onto our Charge Point Management System (CPMS).
• End-to-end EV charger lifecycle management, including deployment, commissioning, monitoring, maintenance, and decommissioning activities.
• Provide technical guidance and support to DC specialists during the commissioning of EV charging solutions.
•  Work closely with Shell, Engineering, and IT colleagues to ensure projects are completed on time and to specification.
• Act as a liaison with the Engineering Procurement Contract Management (EPCM) partner to manage projects from start to finish, ensuring charge points are successfully onboarded on the Charge Point Management System (CPMS).
• Collaborate with development, operations and support  teams to build scalable and resilient systems.
• Contribute to incident response, root-cause analysis, and post-mortem reviews, driving continuous improvement.
• Participate in capacity planning, performance tuning, and resource optimization.
• Integrate security and compliance best practices into all infrastructure operations.
• Stay current with emerging SRE tools, frameworks, and cloud technologies to continuously improve reliability practices.
• Participate in and lead on-call rotations and incident response, conducting detailed postmortems and RCA reports.
• Flexible to resolve blocking issues during off hours or weekends if required.  

What We’re Looking For: 

Basic Qualifications and skills

• Bachelor’s degree in Engineering , Electrical, ECE, Computer Science, Information Technology, or related field.
• Overall 1+ years of experience as a Site Reliability Engineer, DevOps/ Technical project coordinator role.
• Proven experience of DevOps, Platform Engineer, SRE or Technical Project Coordination with IoT or connected devices based platforms.
• Experience with incident management and on-call best practices. Provide support to on call engineers
• Manage and maintain AWS cloud infrastructure to ensure high availability, scalability, reliability, and performance of the EV charging platform.
• Use Terraform (Infrastructure as Code) to automate infrastructure provisioning, configuration, and environment management.
• Configure and manage load balancing to distribute traffic efficiently across platform services, improve application availability, and eliminate performance bottlenecks.
• Excellent analytical and problem-solving skills with a proactive mindset. 
• Expertise with monitoring and observability tools (Dynatrace,Prometheus, Grafana, Zabbix, etc.).
• Proactively monitor the network, triage performance outliers, and coordinate correction actions to ensure optimal system functionality.
• Fluency in English (spoken and written). 
• Successfully recommission or decommission chargers following changes in our network.
• Responsible for the go-live of the chargers on Shell’s public network following commissioning attempts.

 Note: This role involves managing infrastructure for a global platform operating in over ten countries, requiring effective communication and collaboration across regions. Strong verbal and written communication skills, along with availability and flexibility to resolve blocking issues, are essential to support On-call Engineers. This role may involve EU or US time‑zone shifts based on business requirements. The shift timing will be 2 PM IST to 11 PM IST.  

What We Offer

• Work with some of the brightest minds in the emerging EV industry.
• Make a tangible impact in reducing carbon emissions and enabling sustainable energy.
• Freedom to suggest, implement, and innovate on systems, processes, and technologies.
• Daily ownership in a high-growth, challenging environment.
• Flexible work environment with hybrid schedules and virtualization options.
• Competitive pay and benefits including health coverage, innovative PTO program, and performance bonuses.

We are looking for a hands-on Senior AWS Cloud Engineer to lead the infrastructure build, optimization, automation, and production deployment of a Multi-Agent AI Chatbot Platform hosted on AWS. The development environment is already in place, and the successful candidate will drive the solution through testing, integrations, and production go-live.

Key Responsibilities

• Review, validate, and optimize existing Terraform code and AWS infrastructure.
• Establish and manage integrations with enterprise platforms such as ServiceNow, Workday, and other third-party systems.
• Design, build, and support secure, scalable, and highly available AWS environments.
• Implement and automate CI/CD pipelines and Infrastructure-as-Code practices.
• Lead infrastructure testing, performance tuning, and production readiness activities.
• Drive deployment and operationalization of the platform in the Production environment.
• Implement cloud governance, security, monitoring, and FinOps best practices.
• Troubleshoot and resolve complex cloud infrastructure issues.

Required Skills & Experience

• 10+ years of IT experience with strong expertise in AWS Cloud Engineering.
• Proven experience designing, deploying, and managing AWS production environments.
• Strong hands-on experience with Terraform and Infrastructure-as-Code.
• Experience with CI/CD pipeline automation and DevOps practices.
• Expertise in AWS services including VPC, IAM, EC2, S3, Lambda, CloudWatch, and networking.
• Experience in performance optimization, reliability, and cloud cost management (FinOps).
• Strong scripting and automation skills.
• Experience integrating enterprise applications through APIs and secure connectivity patterns.

Strong Data Engineer (AWS) Profile

2

Mandatory (Experience 1) – Must have minimum 6+ years of experience in Data Engineering, with strong experience designing, developing, and maintaining scalable data pipelines and ETL processes.

3

Mandatory (Experience 2) – Must have 3+ years of hands-on experience with AWS data engineering services, particularly S3, AWS Glue, EMR, Lambda, and Redshift.

4

Mandatory (Experience 3) – Must have strong programming experience in Python, Java, or Scala, with hands-on development of data processing and pipeline solutions.

5

Mandatory (Experience 4) – Must have strong expertise in SQL, data warehousing, and database technologies, including experience with both SQL and NoSQL databases.

6

Mandatory (Experience 5) – Must have hands-on experience working with Big Data technologies and distributed data processing, including designing and optimizing large-scale data pipelines.

7

Mandatory (Experience 6) – Must have experience with data pipeline troubleshooting, monitoring, data quality, governance, and security best practices.

8

Mandatory (Education) – B.Tech / B.E./ M.Tech

Role Overview

We are looking for a DevOps / Platform Engineer with 3–4 years of experience to help manage AWS cloud infrastructure, automation, and AI-ready platform operations. You will work closely with engineering and AI teams to ensure scalable, secure, and reliable deployments.

Key Responsibilities

1. AWS & Infrastructure: Manage core AWS services (EC2, EKS, Lambda, RDS, S3, VPC) using Infrastructure-as-Code (Terraform, CloudFormation, or CDK).
2. AI/ML Workflows: Support infra for AI applications, model serving, vector databases, and GPU-based workloads.
3. CI/CD & Automation: Build and maintain deployment pipelines using GitHub Actions, GitLab CI, or Jenkins.
4. Containers & Kubernetes: Deploy and maintain containerized applications using Docker and Kubernetes (EKS) or ECS.
5. Observability & Incident Support: Maintain dashboards and alerts with CloudWatch, Prometheus, or Grafana, and participate in on-call response.
6. Security & FinOps: Apply AWS security best practices (IAM, encryption, secrets) and assist with cloud cost optimization.

Must-Have Skills & Qualifications

1. 3–4 years of experience in DevOps, SRE, or Cloud Infrastructure Engineering.
2. AWS & Containers: Good working knowledge of core AWS services, Docker, and Kubernetes (EKS).
3. Infrastructure-as-Code: Practical experience with Terraform, CloudFormation, or AWS CDK.
4. Linux & Networking: Solid fundamentals in Linux systems, DNS, routing, and load balancing.
5. Scripting: Hands-on scripting proficiency in Python, Bash, or Go.
6. CI/CD & Observability: Experience building CI/CD pipelines (GitHub Actions, GitLab CI, or Jenkins) and using monitoring tools (CloudWatch, Prometheus, Grafana, or Datadog).

Nice-to-Have Skills

1. Basic exposure to AI/ML infrastructure, model serving, or GPU-based compute.
2. Experience with event-driven messaging systems (Kafka, Redis, or SQS).
3. Familiarity with GitOps tools and Helm (ArgoCD, Flux, or OpenTelemetry).
4. AWS Certifications (Solutions Architect Associate, SysOps Administrator, or DevOps Engineer).

Strong Data Engineer (AWS) Profile

2

Mandatory (Experience 1) – Must have minimum 6+ years of experience in Data Engineering, with strong experience designing, developing, and maintaining scalable data pipelines and ETL processes.

3

Mandatory (Experience 2) – Must have 3+ years of hands-on experience with AWS data engineering services, particularly S3, AWS Glue, EMR, Lambda, and Redshift.

4

Mandatory (Experience 3) – Must have strong programming experience in Python, Java, or Scala, with hands-on development of data processing and pipeline solutions.

5

Mandatory (Experience 4) – Must have strong expertise in SQL, data warehousing, and database technologies, including experience with both SQL and NoSQL databases.

6

Mandatory (Experience 5) – Must have hands-on experience working with Big Data technologies and distributed data processing, including designing and optimizing large-scale data pipelines.

7

Mandatory (Experience 6) – Must have experience with data pipeline troubleshooting, monitoring, data quality, governance, and security best practices.

8

Mandatory (Education) – B.Tech / B.E./ M.Tech

• 3 years of relevant experience in DevOps, Site Reliability Engineering (SRE), cloud operations, or application production support.
• Strong practical understanding of AWS cloud services, including compute, storage, IAM, networking, security, and monitoring.
• Good Linux administration skills, including system troubleshooting, performance analysis, log investigation, and service management.
• Hands-on experience with GitLab CI/CD, including pipeline configuration, execution, troubleshooting, YAML files, variables, artifacts, and GitLab Runners.
• Mandatory hands-on experience with Infrastructure as Code (IaC), preferably Terraform, including infrastructure provisioning, configuration management, reusable modules, state management, and troubleshooting deployment issues.
• Working knowledge of Git, branching strategies, merge requests, and version control workflows.
• Good understanding of networking fundamentals, including DNS, TCP/IP, ports, routing, load balancers, and AWS security groups.
• Ability to systematically investigate and resolve infrastructure, application, and deployment issues using logs, metrics, monitoring tools, and dependency analysis.
• Working knowledge of Python or Shell scripting for operational automation and troubleshooting.
• Strong problem-solving skills, communication, collaboration, willingness to learn, and ownership of incidents through resolution.

Preferred Skills and Experience

• Basic SQL knowledge for application and database-related operational investigations.
• Experience with AWS monitoring and logging services, particularly Amazon CloudWatch.
• Familiarity with incident, problem, and change management processes in production environments.
• Exposure to operational automation, proactive monitoring, alert optimization, and root cause analysis (RCA).
• Understanding of cloud infrastructure best practices, including security, reliability, availability, and cost optimization.
• Experience supporting containerized applications using Docker, Kubernetes, or Amazon EKS would be an advantage.

Forward-deployed engineers (FDEs) are Mactores' services layer. You embed with the customer's team, own outcomes from discovery through the production cutover, and personally carry the delivery commitment.

The agent platform we deploy absorbs 60–70% of engagement work, discovery, assessment, design, and testing. You absorb the judgment: target architecture, refactoring trade-offs, model selection, cutover strategy, and the decisions an agent platform cannot make. The agent absorbs scale. You absorb judgment. 

This is not a staff-augmentation seat and not an advisory role. You ship.

What you will do?

• Deliver production agentic AI systems and AWS modernization engagements on committed dates across three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
• Build and productionize AI agents, orchestration, retrieval pipelines, evaluation harnesses, observability running against real customer data, not demo data.
• Convert existing products into agents: expose product functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Convert existing Business processes into agents: expose process functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Embed directly with customer engineering teams. Run architecture sessions, defend design decisions, and align stakeholders from VP Engineering to CTO.
• Make agent decisions traceable and defensible, validation runs in parallel with live workloads, and outputs hold up to internal audit and regulators (HIPAA, PCI-DSS, FSI-grade governance where the vertical demands it).
• Feed field experience back into the platform and practice: your deployment patterns, integration playbooks, and edge cases shape how we deliver.

What are we looking for?

• Excellent communication skills (English) — verbal and written. Non-negotiable. You will present architecture to customer CTOs, write documents that hold up in audit, and defend judgment calls in the room. If you can build but not explain, this role is not a fit.
• You have shipped production agentic AI systems on AWS. Not POCs, not notebooks — systems running in production for real users. This is the primary qualification. Be prepared to walk through what you shipped, the decisions you made, and what broke.
• Deep understanding of agentic architecture — you can design an agent system from first principles and explain why each component exists:
• Agent design patterns: single-agent vs. multi-agent systems, supervisor/orchestrator patterns, hierarchical agent topologies, planner–executor separation, and when each applies.
• Orchestration: building and operating orchestrator agents that decompose tasks, route work to specialist agents or tools, and manage state across multi-step workflows (LangGraph, Strands Agents, CrewAI, or equivalent).
• Memory: short-term/working memory (context management, conversation state) and long-term memory (episodic and semantic stores, vector- and graph-backed retrieval), and the production trade-offs of each.
• Reflection and self-correction: critique loops, self-evaluation, retry-with-feedback patterns, and evaluation harnesses that catch agent failures before customers do.
• Tool use and function calling: schema design, tool-selection reliability, error handling, and agent-to-agent composition.
• RAG and retrieval pipelines: chunking, embedding, hybrid retrieval, reranking, and grounding agent decisions in customer data.
• Strong AWS production experience: Amazon Bedrock and AWS AI services, plus core platform services (Lambda, API Gateway, DynamoDB, RDS/Aurora, Glue, EMR, Redshift, Kinesis, or similar depending on specialization).
• Solid software engineering fundamentals Python, TypeScript, CI/CD, infrastructure-as-code, testing-driven development discipline.
• Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads.
• Indicative experience: roughly 3–10 years in engineering roles, with agentic AI / GenAI as your current day job. We have demonstrated agent-native expertise over tenure — an engineer with 3–4 years of hands-on agentic AI work typically outperforms a 12-year generalist on this work.

You'll be preferred if you've:

• US English verbal and written fluency 
• Delivery experience in one or more of our verticals: Financial Services, Healthcare & Life Sciences, Internet & Software, Manufacturing, or Telco/Media/Entertainment/Gaming/Sports.
• Model tuning and fine-tuning: systematic prompt engineering and optimization; parameter-efficient fine-tuning (LoRA/QLoRA or similar); instruction tuning; working knowledge of RLHF/DPO; sound judgment on when to fine-tune vs. prompt vs. RAG; and evaluation of tuned models against baselines. Fine-tuning experience on Amazon Bedrock or SageMaker is a plus.
• Experience with compliance-sensitive AI systems (HIPAA, PCI-DSS, SOC 2, data residency).
• Knowledge graph, code-analysis (AST), or CDC/streaming experience (Debezium, Kafka/MSK).
• Solid software engineering fundamentals — Java, C++, Go Lang, .Net, Rust
• Prior customer-facing consulting or forward-deployed experience.
• AWS certifications (Solutions Architect Professional, Machine Learning Specialty, or Data Analytics).

Why This Role?

• You own outcomes, not tickets. FDEs carry the delivery commitment personally — architecture, judgment, and cutover are yours.
• You work agent-native from day one. Our delivery model would not function without agents. You build with the platform, not around it.
• You ship. Engagements measured in weeks to production, legacy retired, outcomes named. No archived pilots.
• You compound. Field delivery informs the Aedeon platform roadmap; the platform's growth expands what you can deliver. Few engineering roles sit in that loop.