Growth Strategy Executive

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.

About the role

You bring experience from organizations where marketing analytics is taken seriously—whether that’s a global technology company, an Indian consumer tech company, a data-driven marketing organization, or a specialist analytics firm. You understand the discipline of marketing science and the craft of building teams that do it well.

Required Qualifications

• 8+ years of hands-on experience in data science, marketing analytics, or applied measurement, Deep expertise in Marketing Mix Modeling (MMM): methodology, tooling, business integration, and stakeholder communication.
• Strong knowledge of marketing attribution, incrementality testing, and causal inference methods (geo-testing, diff-in-diff, synthetic controls, uplift modeling).
• Experience with performance forecasting and budget optimization in a marketing context.
• Proven ability to manage and influence senior stakeholders across functions and geographies.
• Strong executive communication skills: comfortable presenting complex methodology and business implications to senior marketing and business leaders.
• Deep proficiency in SQL and Python or R; experience operating in production analytics environments.
• Comfort operating in a globally distributed team environment with US-based counterparts.

Nice to Have

• Exposure to offline marketing analytics, brand measurement, or upper-funnel attribution.
• Experience scaling and modernizing legacy marketing measurement infrastructure.
• Familiarity with dbt, Databricks, Snowflake, Airflow, or comparable data infrastructure.
• Experience building or managing analytics teams across multiple geographies.
•  

What Sets You Apart

• You build measurement frameworks that marketing leaders actually use to make decisions—not just deliver reports
• You communicate across the spectrum—from walking an analyst through a causal design to presenting budget implications to a CMO
• You hold a high bar for analytical credibility while being pragmatic about delivery timelines
• You build strong cross-geography partnerships 
• You stay technically grounded even as your scope grows—you know when to go hands-on and when to step back

·      Knowledge of RIL Automation tools : Jio Track, Tableau

·      Preventive and Corrective Maintenance of Small Cell, Wifi, UBR + UBR (Air Fiber Back Haul), L2 Switch, OSP / Common

·      Preventive Maintenance of

o  IBS (Indoor Small Cell/WiFi/UBR (P2P/P2MP)) : Battery & SMPS, Electrical Panel, Energy Readings of energy meters, Earthing & Power System, Alarm extension to NOC, Security Alarm System testing.

o  Small Cell / L2 Switch / UBR All Type / WiFi : Power/CIPRI cable, GPS Connector, Antenna & LOS alignment,  etc

o  SMPS Maintenance : Rectifier Ventilation cleaning, LVD/BLVD setting check, Check All Parameters visible on NOC, Hardware Replacement - Rectifier, Controller, Communication

o  Battery Maintenance : Battery Discharge test, Voltage & abnormal temperature, Earthing & Power connection.

o  Earthing Maintenance : Earth pit Measurement and Maintenance, Record Voltage between Neutral & Earthing, Check earthing bonding at all points at site.

o  Overall hygiene of site

·      Replacing Faulty cards with Healthy Spares at site - Bring faulty Module from Site to CMP .

·      Providing data for Consumption booking in SAP - Related tasks as instructed by NOC Engineers Small cells, WIFI, IP, SMPS, Battery, Earthing & UBR Maintenance.

·      Should be well versed with basic Microsoft Office.

Job Description: Data Analyst

Position: Data Analyst

Location: Gurgaon

Experience Level: 1-3 Years

Employment Type: Full-Time

Role Overview

We are looking for a results-driven Data Analyst to join our team and support business decision-making through data insights and analytics. The ideal candidate will be highly proficient in Python, skilled in data visualization tools, and experienced in solving complex problems to drive measurable business outcomes such as revenue growth or cost reduction.

Key Responsibilities

Data Analysis and Insights:

Extract, clean, and analyze large datasets using Python to uncover trends and actionable insights.

Develop predictive models and conduct exploratory data analysis to support business growth and operational efficiency.

Business Impact:

Identify opportunities to increase revenue or reduce costs through data-driven strategies.

Collaborate with stakeholders to understand business challenges and provide analytics-driven solutions.

Data Visualization:

Build intuitive dashboards and reports using tools like Zoho Analytics, Looker Studio, or Tableau.

Present findings and insights clearly to both technical and non-technical stakeholders.

Problem-Solving:

Work on end-to-end problem-solving, from identifying issues to implementing data-backed solutions.

Continuously optimize processes through automation and advanced analytics techniques.

Collaboration and Reporting:

Work closely with teams across departments to understand data needs and deliver solutions tailored to their goals.

Provide ongoing reporting and insights to track key performance indicators (KPIs) and project outcomes.

Required Skills & Qualifications

Technical Expertise:

Strong proficiency in Python, including libraries such as Pandas, NumPy, Matplotlib, and Seaborn.

Hands-on experience with BI tools like Zoho Analytics, Looker Studio, or Tableau.

Analytical Skills:

Proven ability to analyze data to generate insights that drive decision-making.

Demonstrated success in addressing business challenges and achieving results such as revenue increment or cost reduction.

Problem-Solving:

Experience working on real-world business problems, identifying root causes, and implementing data-based solutions.

Communication:

Strong ability to communicate complex insights effectively to diverse audiences.

Excellent presentation and storytelling skills to translate data into actionable business strategies.

Preferred Qualifications

Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or related field.

Certifications in data analytics tools or platforms (e.g., Tableau, Looker).

Experience with advanced analytics or machine learning concepts.

What We Offer

Opportunity to work on impactful projects that directly influence business outcomes.

Collaborative, innovative, and supportive work environment.

Access to cutting-edge tools and technologies.

Competitive salary and growth opportunities.

Job Title: Lead Generation Trainee 

Overview: As a Lead Generation Trainee, you will be responsible for identifying and qualifying potential leads for our company's products or services. This entry-level position provides an opportunity to learn and develop essential skills in sales and marketing, particularly in the area of lead generation. You will work closely with the lead generation team to understand strategies, tools, and techniques used to identify and engage potential customers. 

Qualifications: 

• Bachelor's degree in business administration, Marketing, or related field preferred. 
• Strong communication skills, both verbal and written. 
• Ability to research and analyze information effectively. 
• Excellent organizational and time management abilities. 
• Basic understanding of sales and marketing concepts. 
• Proficiency in Microsoft Office suite and CRM software. 
• Self-motivated with a desire to learn and grow in the field of lead generation. 

Benefits: 

• Comprehensive training and mentorship program. 
• Opportunity for career advancement within the lead generation team or other areas of the company. 
• Competitive salary and benefits package. 
• Exposure to cutting-edge tools and technologies used in lead generation. 
• Dynamic and collaborative work environment.

Requirements and Qualifications :
- BS/MS degree in Computer Science, Engineering or a related subject
- 3-5 years of experience with Ruby and the Rails framework
- 3-5 years of experience with Git and Github
- 3-5 years of experience with SQL backends, preferably MongoDB, PostGre SQL
- Familiarity with testing tools such as Rspec
- Knowledge of Docker and docker-based deployment

- Critical thinker and problem-solving skills
- Team player
- Good time-management skills